ISP T raffic Management via Flo w
Optimization
vorgelegt von
Dipl.-Ing.
Emmanuel Obi Akonjang
geb. in Victoria
von der Fakultät IV – Elektrotechnik und Informatik
der Technischen Universität Berlin
zur Erlangung des akademischen Grades
Doktor der Ingenieur wissenschaften
- Dr.-Ing. -
genehmigte Dissertation
Promotionsaussc h uss:
V orsitzender: Prof. Dr. habil. Thomas Zinner, T ec hnisc he Univ ersität Berlin, German y
Gutac h ter: Prof. Georgios Smaragdakis, Ph. D., T ec hnisc he Univ ersität Berlin, German y
Gutac h terin: Prof. Anja F eldmann, Ph. D., Max Planc k Institute for Informatics, German y
Gutac h ter: Prof. Dr. Olaf Maennel, T allinn Univ ersit y of T ec hnology , Estonia
T ag der wissensc haftlic hen A ussprac he: 21.12.2018
Berlin 2019

Eidesstattliche Erklä rung
Ic h v ersic here an Eides statt, dass ic h diese Dissertation selbständig v erfasst und
n ur die angegeb enen Quellen und Hilfsmittel v erw endet hab e.
Datum Emman uel Obi Ak onjang (Dipl.-Ing.)
i

Abstract
A ma jor c hallenge that In ternet Service Pro viders (ISPs) face to da y , is the gro wing
traffic v olume that they m ust handle. The situation b ecomes ev en more complex, as
users’ demands b ecome more v olatile or when new application trends emerge that
fundamen tally c hange traffic dynamics and comp osition. Th us, ISPs are alw a ys
comp elled to deal with suc h c hanges, in a prop er, efficient, and timely manner.
One t ypical example of suc h a trend, which has a profound effect on traffic v ol-
ume, traffic comp osition, traffic dynamics, o verall net w ork p erformance and user
exp erience, is P eer-to-P eer (P2P). P2P’s disruptiv e and high bandwidth-consuming
nature, p oses a n um b er of c hallenges to an ISP’s capabilit y to effectiv ely manage
traffic on its o wn net w ork. This is a ma jor concern/issue that calls for quic k and
effectiv e mitigation approac hes by ISPs. The mitigation pro cess starts with studying
and understanding P2P systems and their proto cols.
Studies rev eal a n um b er of imp ortant c haracteristics and shortcomings of P2P sys-
tems, including: i) high ch urn, ii) selfish construction and main tenance of the P2P
o v erlay , iii) una wareness of the underlying net w ork conditions, iv) mismatch betw een
P2P o v erlay and ISP underla y , v) high managemen t traffic, vi) high prop ortion of
cross-AS neigh b or-relationships and cross-AS traffic, vii) a data flo w b et w een tw o
p eers often crosses AS b oundaries m ultiple times.
Based on the ab o ve, in this thesis, w e prop ose a solution that promotes collab oration
b et ween the P2P o v erla y and the ISP underla y , leading to a win-win situation for
b oth parties. In detail, w e prop ose an ISP-op erated v alue-added service that w e call
the Or acle . The Oracle pro vides an in terface for p eers to b ecome a w are of netw ork
conditions, th us improv e p eer selection and p erformance of P2P applications. The
Oracle do es this b y sorting a p eer’s list of p otential neigh b ors/do wnload sources to
fa v or lo calit y , i.e. p eers that are in the same AS domain and ev en in the same geo-
graphical lo cation lik e the requesting p eer. With the Oracle, we sho w that impro v ed
p erformance that b enefits ISPs, applications and users are attainable.
W e conduct a series of pac ket-lev el sim ulations to study and quan tify p otential gains
offered b y the Oracle service. W e start with an implemen tation of the Gn utella P2P
proto col within the SSFNet sim ulation en vironmen t. W e next mo del differen t ISP
and P2P top ologies, to study the effects of the Oracle across div erse top ologies,
then use v arious mathematical distributions to mo del user b eha vioral patterns that
reflect realistic, inauspicious and b est-case scenarios. Using appropriate metrics,
w e quan tify and compare the p erformance b etw een un biased (no use of the Oracle
service) and biased (use of the Oracle service) top ologies. In nearly all categories,
our analyses rev eal sup erior p erformances for Oracle-biased top ologies.
W e next shift our fo cus to backbone net w orks and study ho w their top ologies affect
general net w ork and application p erformance in the presence of normal and hea vy
traffic load. F or this, w e use a reference bac kb one net w ork mo del for German y
iii

and deriv e 3 differen t top ologies from it. By k eeping the n um b er of no des (PoPs)
in the top ologies constan t at 12 and v arying the num b er (and size) of their links
from 66 (fullmesh), to 30 and 20 resp ectiv ely , w e obtain 3 top ologies that differ
in capacit y (total bandwidth) and other top ological/structural prop erties. W e use
selected metrics to assess and compare their p erformance under the same traffic
conditions. Our analyses show similarities as w ell as differences in p erformance
b et ween the topologies. W e observ e that, for a few categories at baseline traffic, the
p erformance in the 20 links top ology (which has the smallest n um b er of links and the
least total bandwidth) is comparable to those in the other 2 top ologies. Ho w ever,
when the traffic increases b y 35%, the p erformance in the 20 links top ology w orsens
and b ecomes the least in nearly all compared categories.
A deep er analysis of the 20 links top ology reveals that, while some ma jor (high
bandwidth) links are suffering from congestion, other links, mostly of lesser band-
width, hav e little to no traffic on them. Instead of upgrading the congested links,
lik e most ISPs w ould normally opt to do, w e prop ose a more efficien t and cost effec-
tiv e solution. It in v olv es the use of mathematical optimization to influence traffic
flo ws and ac hieve better netw ork and application p erformance. W e argue and sho w
that b etter p erformance is attainable b y minimizing the maxim um link utilization
and efficien tly distributing the traffic load across all links in the top ology . Our main
ob jectiv es are congestion a v oidance in the ISP net w ork, p erformance enhancemen t
for applications that run on the ISP net w ork and b etter cost con trol b y the ISP .
iv

Zusammenfassung
Eine große Herausforderung für In ternetdienstan bietern (ISP s) im In ternet, ist die
w ac hsende Menge an V erk ehr, die sie stemmen m üssen. Die Lage wird komplexer,
als die Nac hfrage der Ben utzer/Kunden volatiler wird oder wenn p opuläre An w en-
dungstrends en tstehen, die Dynamik und K omp osition des V erkehrs fundamen tal
v erändern. ISPs sehen sic h gezwungen mit solc hen V eränderungen in angemessener,
effizien ter und zeitgemäßer Art und W eise umzugehen.
Ein t ypisc hes Beispiel eines solc hen T rends, w elc her maßgeblic he A uswirkungen auf
Menge, K omp osition, und Dynamik v on V erk ehr, Leistungsfähigk eit des Netzw erks,
und User Exp erience hat, ist P eer-to-P eer (P2P). Die disruptiv e und hohe Bandbrei-
tenk onsumierende Natur v on P2P stellt die Fähigk eit v on ISPs, das eigene Netzw erk
zu managen, v or einige Herausforderungen. Es stellt eine Hauptsorge der ISPs dar
und erforderte sc hnelle und effektiv e Abmilderungsansätze. Solc he Abmilderungs-
prozesse b eginnen mit der Un tersuch ung und dem V erständnis v on P2P-Systemen
und ihrer Protok olle.
Studien bringen einige wic h tige Charakteristik en und Defizite v on P2P-Systemen
herv or, un ter anderem: i) hohen Churn. ii) eigenn ützige Erric h tung und W artung des
P2P Ov erla ys iii) Un wissenheit des Underla y Netzw erkzustandes iv) Diskrepanz w e-
gen mangelnden Zusammenhang zwisc hen dem P2P-Ov erla y und dem Underla y des
ISPs v) hoher Signalingo v erhead, die zu zusätzlichen V erk ehr führt. vi) hoher An teil
an Cross-AS-Nac h barschaftsbeziehungen und Cross-AS-V erkehr vii) oft, der Daten-
fluss zwisc hen einer Quelle und einem Ziel üb erquert mehrmals die AS-Grenzen.
Basierend auf diesen F akten, sc hlagen wir eine Lösung v or, die die Kollaboration
zwisc hen P2P-Ov erlay und ISP-Underla y fördert, und b eiden Seiten V orteile bringt.
Wir sc hlagen einen v om ISP b etrieb enen, Mehrw ert-Service v or, den wir Or acle nen-
nen. Das Oracle bietet eine Sc hnittstelle, w omit P eers, den Zustand des Netzw erks
erkundigen k önnen, um eine V erb esserung der P eer-Ausw ahl und der P erformance
v on P2P-An wendungen zu ermöglic hen. Dab ei sortiert es eine Liste v on p oten tiel-
len Nac h barn/Do wnloadquellen der P eers, basierend auf deren örtlic hen Lage, d.h.,
P eers die sic h in der selb en Domain und in der selb en geografisc hen Lage b efin-
den, wie der anfordernde P eer. Unser Hauptziel ist es die ob en genann ten Probleme
abzumildern und zugleic h dem ISP und den P2P-Ben utzern V orteile zu bieten.
Wir führen mehrere P ac ket-Lev el-Sim ulationen durc h, um die p otentiellen V orteile,
die das Oracle zu bieten hat, zu studieren und zu quan tifizieren. Wir b eginnen mit
einer Implemen tierung des Gn utella P2P-Protokolls innerhalb der SSFNet Sim ulati-
onsumgebung. Als näc hstes mo dellieren wir versc hiedene ISP- und P2P-T op ologien,
um die A uswirkungen des Oracles auf un tersc hiedlic he T op ologien zu studieren,
gefolgt v on der Mo dellierung von Ben utzerv erhalten, die realistisc he, ungünstige
und b est-case Szenarien, mittels v ersc hiedener mathematisc her V erteilungen, dar-
stellen. Wir quan tifizieren und v ergleichen die Leistung v on nic h t-mo difizierten (oh-
v

ne Oracle-Service) und mo difizierten (mit Oracle-Service) T op ologien. In fast al-
len Kategorien bringen unsere Analysen eine erhöh te Leistung b ei T op ologien mit
Oracle-Un terstützung herv or.
Als näc hstes, setzen wir unseren F okus auf Backbone-Netzwerk e und analysieren,
wie deren T op ologien sich auf die all gemeine Netzw erk- und Applikationsleistung,
in Gegen w art v on normaler und stark er V erk ehrslast, auswirk en. Dazu v erw enden
wir ein Referenznetzw erkmo dell für Deutschland und leite n daraus 3 un tersc hiedlic he
T op ologien ab. Während wir die Anzahl der Knoten (P oPs) in den T op ologien k on-
stan t b ei 12 halten, und die Anzahl (und Größe) ihrer Links v ariieren, also zwisc hen
66 (fullmesh), 30 und 20, erhalten wir 3 T op ologien, die sic h in Kapazität (gesam te
Bandbreite) und anderen top ologisc hen/strukturellen Eigenschaften un tersc heiden.
Wir v erw enden ausgew ählte Metrik en um die Leistung un ter gleic hen V erk ehrsb e-
dingungen zu ermitteln und zu v ergleic hen. Unsere Analysen zeigen Ähnlic hkeiten
und Un tersc hiede in der Leistung zwischen den T op ologien. Wir b eobac h ten, dass
die Leistung in der 20-Links-T op ologie (welc he die w enigstens Links und die kleinste
Gesam tbandbreite b esitzt) nur in einiger w eniger Kategorien v ergleich bar ist mit
denen der anderen b eiden T op ologien. Und zw ar, nur bei Baseline-V erk ehr. Bei Er-
höh ung des V erkehrs um 35%, v erringert sic h die Leistung in der 20-Links-T op ologie
in fast allen Kategorien zur geringsten.
Eine genauere Analyse der 20-Links-T op ologie zeigt, dass während einige große Links
(mit hohen Bandbreite) v on V erkehrsstau beeinträc h tigt sind, andere Links, meist
mit w eniger Bandbreite, w enig bis k ein V erk ehr hab en. Anstatt die b eein träc h tigen
Links auszubauen, wie die meisten ISPs es tun würden, empfehlen wir eine effizien-
tere und k osteneffektiv ere Lösung. Es b einhaltet die Nutzung mathematisc her Op-
timierungen zur Beeinflussung des V erk ehrs und der Realisierung b esserer Leistung.
Wir glaub en und zeigen, dass die V erb esserung der Leistung, durc h Minimierung der
maximalen A uslastung der Links und effizien ter V erteilung der V erk ehrslast üb er al-
le Links in der T op ologie, erreich t w erden kann. Unsere Hauptziele sind V ermeidung
v on V erk ehrsstau im ISP-Netzw erk, Leistungssteigerung für An w endungen, die das
ISP-Netzw erk v erw enden und b essere K ostenk on trolle durc h den ISP .
vi

A ckno wledgments
Whatever you do [no matter what it is] in wor d or de e d, do everything in the name of
the L or d Jesus [and in dep endenc e on Him], giving thanks to Go d the F ather thr ough
Him.
Colossians 3:17 (AMP)
All things are p ossible with Go d. I thank Him for m y life and for His divine in ter-
v en tion in m y c hallenges and battles, esp ecially during the p erio d of this pro ject.
This thesis w ould not ha v e b een p ossible, if not of the p eople He sen t to guide,
advice, encourage and supp ort me.
First and foremost, I w ould like to specially and sincerely thank m y advisor, men tor
and guide, Prof. Anja F eldmann, for her relen tless supp ort, inspiring tec hnical
advice, constan t encouragemen t and great patience throughout the duration of this
pro ject. She alw a ys made time for us to discuss m y progress, p oin ting me to the
righ t ideas, relev an t literature and p eople, whenev er I had needed them (whic h w as
always ). I am v ery grateful to ha ve le arned and ric hly b enefited from her metho dical
and practical approac h to researc h and her atten tion to details. Her easygoing nature
is simply exemplary and v ery inspiring. I really can not thank her enough for all
she has done and the role she has pla y ed in m y life.
I w ould also lik e to heartily thank Prof. Georgios Smaragdakis, the co-advisor and
m y men tor, for the uncoun table hours of tec hnical and non-technical advice, discus-
sions and encouragemen ts that he has offered me o v er the y ears. I ha ve benefited a
lot from his deep insigh t and exp ertise. He alw a ys made time for me, ev en when he
w as pressed with other imp ortan t activities. I am v ery grateful for all his help.
My thanks also go to Prof. Stev e Ulig for the initial discussions w e had on bac kb one
net w orks and netw ork structural prop erties, back in his T-Labs da ys and to Prof.
Thomas Zinner for the discussions and advice he offered at the latter stage of this
pro ject.
Man y thanks to Prof. Olaf Maennel for accepting to b e an examiner of this thesis and
to all the mem b ers of the PhD committee for their precious time sp ent in reviewing
m y w ork.
I’v e had a w onderful time, w orking with former and curren t mem b ers of the In-
telligen t Net w orking (F G-INET) team. I am very grateful for all the discussions,
ideas, ob jectiv e criticisms, help, con tributions and lessons that eac h of them has
offered. I esp ecially thank Vina y Aggarwal, whom I w ork ed closely with and whose
brillian t con tributions b o osted our progress, Matthias Rost, for his in v aluable in-
sigh t, help and discussions on math ematical programming, Thomas Krenc, for the
man y discussions and supp ort, as w ell as his great help with translation into p erfect
vii

German. Man y thanks also to Ingmar P o ese and Benjamin F rank, great colleagues,
with whom I had the privilege and jo y to w ork on some pro jects.
I’m also grateful to the former and curren t A dministrativ e Assistants, Britta Sc hnei-
der and Birgit Hohmeier-T ouré, resp ectiv ely for their ceaseless readiness to help,
esp ecially with bureaucratic follo w-ups. My thanks also go to Rainer Ma y , our able
System A dministrator, who contin uously ensures systems remain up and running.
He is alw a ys ready to help out when things go wrong or when w e mess them up.
Muc h thanks also to Sarah Dierenfeld, for her patience and help, in sorting out the
issues I o ccasionally had with m y accoun t or access to particular systems.
In no particular order, I w ould also lik e to extend sp ecial thanks to some close
relativ es and friends; Serge Ngueda, Dr. Iv an Ndip, Bigoh Ak onjang, Bert Salz
and W alter Agb or Baw a (PharmD), for their encouragemen t, supp ort and pra y ers
o v er the years. I will alw a ys remain grateful to them, esp ecially for their moral and
spiritual supp ort.
Sure and of course, I w ould as well lik e to thank m y wife, Onek e and our c hildren,
Agb or-T oko, Babbey , Mikaili and Sarahila, for their endless lo ve, patience, supp ort
and sacrifices. I really appreciate their understanding, esp ecially whenev er I w as
quite pressed and rep eatedly couldn’t mak e time to join them for family activities,
trips and v acations.
Last but not least, my apologies to all those whose names I could not explicitly men-
tion. Y our con tributions will nev er b e forgotten and I will alw a ys remain thankful
and grateful to y ou as w ell.
viii

Dedication
In dedication to m y late m um, F rida Agb or-T ok o Ak onjang.
Thank y ou m um for the lov e y ou sho w ed me from the day I w as b orn, the lessons
y ou taugh t me, the core v alues y ou instilled in me as a c hild, y our ceaseless
encouragemen ts and y our advice to take on and complete this pro ject.
Unfortunately , y our long battle with cancer started just when this pro ject w as
gathering sp eed. The short up-phases and long do wn-phases that ensued, your
strong will to k eep on figh ting, my restlessness and w orries in seeing y ou go
through so m uc h pains, then the dev astation brough t b y the news of y our passing
a w ay ... no da y go es b y without me thinking of y ou, m um.
I will alw a ys miss you.
ix

Publications
Conferences and W o rkshops
Ingmar Po ese, Obi A konjang, A nja F eldmann, and Ge or gios Smar agdakis
Implemen tation of a Pro xidor Use Case.
T alk at IETF-74, San F r ancisc o, USA, Mar ch 2009
V inay A ggarwal, Obi A konjang and A nja F eldmann
ISP-Aided Neigh b or Selection in P2P Systems
Internet Engine ering T ask F or c e (IETF) P2P Infr astructur e W orkshop, Boston,
USA, May 2008
A nja F eldmann, V inay A ggarwal and Obi A konjang
ISP-Aided Neigh b or Selection in P2P Systems
RIPE 56, Berlin, Germany, May 2008
V inay A ggarwal, Obi A konjang and A nja F eldmann
Impro ving User and ISP Exp erience through ISP-aided P2P Lo calit y
11th IEEE Glob al Internet (GI’08) Symp osium, Pho enix, USA, A pril 2008
V inay A ggarwal, Obi Akonjang, A nja F eldmann, Seb astian Mohrs & R umen T ashev
Reflecting P2P User Beha viour Mo dels in a Sim ulation En vironment
16th Eur omicr o International Confer enc e on Par al lel, Distribute d and Network-b ase d
Pr o c essing (PDP), T oulouse, F r anc e, F ebruary 2008
Internet Drafts
Obi A konjang, A nja F eldmann, Stefano Pr evidi, Bruc e Davie, and Damien Sauc ez
The PR O XIDOR Service
Internet Engine ering T ask F or c e (IETF), Internet Dr aft, Mar ch 2009
Proto col Sp ecifications
Obi A konjang, V inay A ggarwal, A nja F eldmann, Jun Jiang and Pengchun Xie
The Oracle Proto col
TU Berlin, Septemb er 2008
xi

Contents
1 Intro duction 1
1.1 In ternet Gro wth ............................. 1
1.2 Motiv ation ................................ 4
1.3 Problem Statemen t ............................ 6
1.4 T raffic Managemen t Approaches .................... 6
1.4.1 Device Upgrades ......................... 7
1.4.2 Bandwidth Pro visioning ..................... 7
1.4.3 A dding New Links ........................ 8
1.4.4 Flo w Rerouting .......................... 8
1.4.5 Change of T raffic Matrix .................... 9
1.4.6 Flo w Optimization ........................ 9
1.5 Summary of Con tributions ........................ 1 1
1.6 Structural Ov erview ........................... 1 3
2 Overview of the Internet 15
2.1 In ternet Structure ............................ 1 5
2.1.1 Edge and Bac kb one Netw orks .................. 1 8
2.1.2 In ternet Exc hange Poin ts .................... 1 9
2.2 Service Pro viders ............................. 1 9
2.3 The TCP/IP Proto col Suite ....................... 2 1
2.4 In ternet Applications and Services ................... 2 3
2.4.1 Clien t-Serv er Application Mo del ................ 2 3
2.4.2 P eer-to-P eer (P2P) Application Mo del ............. 2 3
2.4.3 P opular Application Services .................. 2 4
2.5 P ac ket F orw arding ............................ 2 6
2.5.1 IP Routing ............................ 2 6
2.5.2 Multiproto col Lab el Switc hing (MPLS) ............ 3 0
2.6 Qualit y of Service (QoS) ......................... 3 1
2.7 Net w ork Measurement .......................... 3 1
2.7.1 A ctiv e Measurement ....................... 3 2
2.7.2 P assiv e Measurement ...................... 3 2
2.7.3 Hybrid Measuremen t ....................... 3 2
2.7.4 Common Metrics ......................... 3 2
2.7.5 Common Measuremen t and Monitoring T o ols ......... 3 4
3 Net w o rk T raffic Management 37
3.1 T raffic Managemen t Challenges ..................... 3 8
3.2 Core Net w ork Architectures ....................... 3 8
3.2.1 Multi-La y er Con trol Plane .................... 4 0
3.2.2 Con v erged Architecture ..................... 4 1
xiii

Con ten ts
3.3 Core Capacit y Planning ......................... 4 2
3.3.1 Bac kb one Net work T op ology .................. 4 2
3.3.2 T raffic Demand Measuremen t .................. 4 3
3.3.3 T raffic Demand F orecast ..................... 4 4
3.3.4 Bandwidth Pro visioning ..................... 4 4
3.4 Net w ork and T raffic Engineering Approac hes ............. 4 5
3.4.1 Soft w are and Hardware Upgrades ................ 4 5
3.4.2 A dditional No des and Links ................... 4 6
3.4.3 Change of T raffic Matrix .................... 4 7
3.4.4 Flo w Rerouting .......................... 4 7
3.5 Net w ork Optimization .......................... 4 8
3.5.1 Routing Limitations ....................... 4 8
3.5.2 Net w ork Graphs ......................... 4 9
3.5.3 Mo deling and Solving Optimization Problems ......... 5 1
4 Managing P2P T raffic via Collab o ration 55
4.1 P eer-to-P eer (P2P) Systems ....................... 5 6
4.1.1 Unstructured P2P Systems ................... 5 6
4.1.2 Structured P2P Systems ..................... 5 6
4.1.3 P erformance Challenges ..................... 5 6
4.1.4 Impro v ements .............. ............. 5 8
4.2 The Oracle Service ............................ 5 8
4.3 The SSFNet Sim ulator .......................... 6 1
4.3.1 Scalable Soft w are F ramew ork (SSF) .............. 6 1
4.3.2 SSFNet Overview ......................... 6 2
4.4 Collab oration within a P2P Sim ulation En vironmen t ......... 6 4
4.4.1 System Design .......................... 6 4
4.4.2 Graph Structural Prop erties of the Ov erla y .......... 6 5
4.4.3 User Exp erience ......................... 6 9
4.5 Effects of T op ology and User Beha vior on Lo calit y .......... 7 1
4.5.1 P erformance Metrics ....................... 7 2
4.6 Ev aluating T op ological Div ersit y .... ................ 7 2
4.6.1 Designing the T op ologies .................... 7 3
4.6.2 Mo deling User Beha vior ..................... 7 4
4.6.3 Sim ulation Results and Analyses ................ 7 7
4.7 Ev aluating Changes in User Beha vior .................. 8 3
4.7.1 A v erage No de Degree and P ath Length of Overla y P eers . . . 83
4.7.2 Queries/Resp onses Analyses ................... 8 4
4.7.3 In tra-AS Con tent Exc hanges and Do wnload Times ...... 8 5
4.8 Bey ond the Oracle Service ........................ 8 5
4.9 Summary ................................. 8 6
xiv

Con ten ts
5 T raffic Effects on Different Backb one T op ologies 89
5.1 Bac kb one T op ologies ........................... 8 9
5.1.1 The F ullmesh T op ology ..................... 9 1
5.1.2 The 30-Links T op ology ..................... 9 1
5.1.3 The 20-Links T op ology ..................... 9 1
5.2 IP T raffic Demand Matrix ........................ 9 1
5.3 Sim ulation Studies ............................ 9 2
5.3.1 The OPNET Mo deler Sim ulator ................ 9 2
5.3.2 System Design .......................... 9 2
5.3.3 T raffic Mo del ........................... 9 3
5.4 Net w ork Performance Analyses ..................... 9 3
5.4.1 P ac kets Hop Coun t ........................ 9 5
5.4.2 Link Throughput and Utilization ................ 9 6
5.4.3 TCP Dela y ............................ 1 0 2
5.4.4 TCP Retransmissions ...................... 1 0 3
5.4.5 R TP Dela y ............................ 1 0 4
5.5 Application P erformance Analyses ................... 1 0 5
5.5.1 FTP Do wnload Resp onse Time ................. 1 0 5
5.5.2 HTTP Receiv ed T raffic ..................... 1 0 6
5.5.3 HTTP Ob ject Resp onse Time .................. 1 0 7
5.5.4 V oice P ack et Jitter ........................ 1 0 9
5.5.5 Video P ac k et End-to-End Dela y ................ 1 1 0
5.6 Summary ................................. 1 1 1
6 Flo w Optimization using Mixed-Integer Programming (MIP) 113
6.1 MIP Problem F orm ulation ........................ 1 1 3
6.2 Solving the MIP Problem ........................ 1 1 5
6.3 Sim ulation Study ............................. 1 1 5
6.4 Results and Analyses ........................... 1 1 6
6.4.1 Throughput and Utilization ................... 1 1 6
6.4.2 P ac ket Hop Coun t ........................ 1 1 9
6.4.3 TCP P erformance ........................ 1 2 0
6.4.4 FTP P erformance ........................ 1 2 3
6.4.5 HTTP P erformance ....................... 1 2 5
6.4.6 V oice P erformance ........................ 1 2 7
6.4.7 Video P erformance ........................ 1 3 1
6.5 Summary ................................. 1 3 4
7 Conclusion 137
7.1 Managing P2P T raffic .......................... 1 3 7
7.2 T op ologies and T raffic Flo ws ...................... 1 3 8
7.3 Optimizing T raffic Flo ws ......................... 1 4 0
7.4 Outlo ok .................................. 1 4 1
xv

Con ten ts
List of Figures 143
List of T ables 145
Bibliography 147
xvi

1
Intro duction
Our so ciet y and life-styles are con tin uously b eing impacted in unpreceden ted wa ys
b y the curren t (knowledge-based and information/data-driv en) digital age. A t the
forefron t of this ev olution is the Internet, whic h is the decisiv e and most influen tial
tec hnology of this era.
The In ternet is a global net w ork, made up of thousands of in terconnected, but
indep enden tly op erated net works of v arious sizes. It started bac k in 1969 as a
researc h pro ject with only a few exp erimen tal no des in one coun try (USA). Nearly
half a cen tury later, it has evolv ed in to a gian t m ulti-purp ose global net w ork with
large n um b ers of no des in ev ery coun try on earth and is still gro wing in size and
functions. Although its administrativ e structure is largely decen tralized, its core
role remains “cen tralized” b y design and function. This fact is eviden t in its role
as the curren t de facto medium for mo dern comm unication and information/data
exc hange. Its global reach and close-to-instan taneous deliv ery sp eeds, ev en b et ween
its furthest p erimeters, makes it the most suitable and preferred medium for modern
fast-paced comm unications. Its p oten tials app ear to b e endless.
The abilit y of the In ternet to accommo date new tec hnologies is a ma jor driv er of
its div ersit y and use as a platform for growing service and application offerings.
It con tin uously transforms and facilitates in multiple w a ys, the differen t means by
whic h v arious facets of our so ciet y (e.g. businesses, go v ernment and educational
institutions, p olitical and non-p olitical organizations, so cial groups and individuals)
in teract with eac h other.
1.1 Internet Gro wth
The In ternet has b een exp eriencing sustained y early gro wths ev er since its transi-
tion from a purely “researc h” net w ork in to an “all-purp ose” (mostly business and
commercial) net w ork. With the in v en tion of the W orld Wide W eb (WWW) some
y ears later, information on the Internet b ecame easily accessible to and explorable
b y billions of p eople, b oasting its p opularit y and gro wth to exp onen tial lev els. This
enormous gro wth is p ortra y ed in the observed rising n um b ers of users, connected
devices, applications and services.
1

Chapter 1 In tro duction
• User P opulation : As of August 2017, there are an estimated 3.819 billion
activ e In ternet users worldwide [ 186 , 187 ], a gro wth of ab out 11.5% from the
previous y ear. This con tinuous gro wth can partly b e attributed to inno v ations
and latest tec hnology-trends that are fostering the dev elopment of new er, b et-
ter and more user-app ealing proto cols, applications and services. A dditional
con tributing factors include m ultiple means of connection (e.g. via fixed lines,
WiFi, mobile (L TE), satellites, etc), faster sp eeds, and falling cost p er band-
width for end-users.
• Connected Devices : The n um b er of connected devices is more than double
that of users. It is estimated to reac h 8.4 billion b y the end of 2017 and 20.4
billion b y 2020 [ 110 ]. Ho w ev er, in terms of mobile connections alone, active
global monitoring sho ws the n um b er of mobile connections and that of unique
mobile subscrib ers to already stand at o ver 8.421 and 5.1 billion respectively , as
of No v em b er 2017 [ 112 ]. The gro wing p opularit y and use of mobile and smart
devices, together with the rapid deploymen t of In ternet of Things (IoT), are
just a few of the catalysts that are helping push these n um b ers to exp onen tial
lev els. IoT is a technology that enables consumer/electronic devices, other
than computers, smart-phones and tablets, to connect to and b e con trollable
via the In ternet.
• Applications and Services : The In ternet’s dynamic and div ersified ser-
vices/applications landscap e a v ails many flexible c hoices, opp ortunities and
b enefits to its users and high rev en ues to its Pro viders. P opular applica-
tions and services, suc h as Peer-to-P eer (P2P) file-sharing, Go ogle searc hes,
Y outub e, F aceb o ok, Netflix and other media consumption/streaming services
are con tributing to observ ed large and ever-increasing traffic v olumes [ 9 , 108 ].
The In ternet’s gro wing p opularit y , so cietal imp ortance, as w ell as its p oten-
tial to accommo date new tec hnologies and services, are together resp onsible
for ma jor shifts in b oth technology and business. F or example, ISPs are re-
thinking their curren t business mo dels and are re-engineering them around the
In ternet. Legacy infrastructures suc h as the circuit-switc hed T elecomm unica-
tion net w orks of y estery ears, are b eing decommissioned and are b eing replaced
b y the In ternet. Services suc h as telephon y and radio/TV broadcasts that used
to run on separate dedicated infrastructures, are no w b eing offered as services
on the In ternet as w ell. An effect of net w ork con v ergence (i.e. providing data,
v oice and video services on the same net work infrastructure) is the massiv e v ol-
umes of additional v oice and video traffic, whic h also need to b e transp orted
across access and bac kb one netw ork infrastructures. When suc h services and
applications b ecome more p opular and are em braced b y even more users, their
traffic p ortions also gro w accordingly , in tensifying the issues already asso ciated
with managing them.
2

1.1 In ternet Gro wth
The size of IP traffic on the In ternet is curren tly estimated to a v erage a colossal 122
Exab ytes p er month 1 . This is exp ected to gro w to 278 Exabytes per month b y the
y ear 2021 [ 109 ]. The In ternet has exp erienced an enormous traffic gro wth in the
past decade, as can b e seen in Figure 1.1 . The global in ternet traffic grew by more
than 22-fold, from an a v erage of 4.234 Exabytes/mon th in 2006 to an a v erage of
96.054 Exab ytes/mon th in 2016 [ 99 – 107 ].
Figure 1.1: Global In ternet traffic gro wth (Source: Cisco VNI, 2008-2017)
T able 1.1: Regional Internet traffic - 2016 y ear-on-y ear p ercen tage growth (Source:
Cisco VNI, 2016 & 2017)
In 2016, global In ternet traffic (measured in Exab ytes p er mon th), grew b y an ap-
pro ximate 32.45%, compared to the previous y ear [ 107 ] [ 109 ]. Regionally , the gro wth
rates v aried b et w een 19.31% (lo w-end) in Cen tral & Eastern Europ e and 38.81%
1 Based on estimates for the y ear 2017
3

Chapter 1 In tro duction
(high-end) in Middle East & Africa. Ho w ev er, North America and Asia P acific still
lead in terms of absolute traffic v olumes, as can b e deducted from T able 1.1 . Suc h
observ ed differences in gro wth are sometimes a result of influential factors that are
also regional in nature. F or example, in North America, the region where the Netflix
Streaming service w as first launc hed, it accoun ted for a large p ercen tage of all do wn-
stream In ternet traffic during p eak times. More than 20% in the US and a significan t
13.5%, four mon ths after it w as first launc hed in Canada [ 208 ]. Similar trends are
observ ed in other regions where the service b ecomes a v ailable as w ell. Netflix’s traf-
fic share con tin ues to grow ev er since, making it one of the dominan t Services on the
In ternet to day , in terms of net work traffic v olumes. A dditional sources of sudden
gro wth and spik es in netw ork traffic, include sp ecial ev en ts, suc h as catastrophes,
w orld sp orting even ts and ev en co ordinated cyb er attacks. In general, long- and
short-term traffic gro wths, as w ell as spik es, can cause congestions in some segmen ts
of the net w ork, leading to dela ys, pac k et drops, jitters and other p erformance-related
issues, if the cause is not addressed in a prop er and timely manner.
1.2 Motivation
Curren t forecasts still predict p ersisten t gro wth in traffic across all categories and
regions, as can b e deduced from Figure 1.2 b elo w.
Figure 1.2: Global In ternet traffic forecast (Source: Cisco VNI, 2017)
The ab o ve graphs sho w the global In ternet traffic forecast for the coming y ears un til
2021, classified b y t yp e (Figure 1.2 a) and geographic region (Figure 1.2 b).
The exp onen tial growth in the n umber of In ternet users and devices is accompa-
nied b y a corresp onding gro wth in the demands for services and applications that
they use. The total v olume of traffic that these together generate also increases,
4

1.2 Motiv ation
thereb y exerting hea vier loads on the infrastructures that supp ort them. T o an ex-
ten t, this gro wing traffic load, together with the large n um b er of applications and
services, do not only imp ose the kind of resources they require from the underlying
infrastructures, they also dictate their exp ected lev els of p erformance and efficiency .
Ho w ever, the rate at whic h these supp orting infrastructures are b eing upgraded to
meet these gro wing demands, often lags b ehind that at whic h the requesting (ac-
cess/user) devices and services are b eing upgraded to tak e adv antage of faster speeds
and latest tec hnological adv ancemen ts. The reasons for the slo wer upgrade-pace of
the underlying infrastructure are man y-fold and include: i) the inflexible design of
these infrastructures, ii) their often v ery complex arc hitectures, iii) their con tin uous
reliance on (mostly) slo w man ual pro cesses for administration and managemen t, iv)
costs.
Still and all, users (customers) on the one hand, exp ect the infrastructure to alw a ys
b e a v ailable and p erform w ell whenev er they need to use it, to remain scalable and
error-free, to b e secure and b e able to adapt to c hanging conditions, irresp ectiv e of
an y c hallenges. On the other hand, rapid gro wth (as observ ed in the n um b ers of
users/devices and the v olumes of traffic flo wing through the net w ork) remains an
incessan t c hallenge to the ISP .
T o ensure Service Lev el Agreements (SLAs) with their customers are k ept and guar-
an teed at all times, ISPs ha v e to pro-activ ely deal with the ab o v e-men tioned issues,
in w a ys that are i) timely , ii) more efficien t, and iii) cost-effective. An SLA is an
agreemen t b etw een an ISP and its customer, in whic h (amongst other things), the
ISP guaran tees that agreed and stated parameters, suc h as a v ailabilit y , dela y , pac k et
loss and jitters, w ould not exceed their con tractually stated v alues.
Despite efforts to plan for and adapt to c hanging usage patterns and trends, ex-
p erience has sho wn that net w ork resources are nev er sufficien t enough. This is
b ecause there are alw a ys new trends, applications or services that will p oten tially
consume whatev er bandwidth they find a v ailable. F or example, the emergence of
P2P , whic h accounts for an exceptionally large proportion of Internet bac kb one traf-
fic [ 114 ] that is also quite c hallenging to plan for, con trol or manage, b ecause of its
unpredictable and disruptiv e nature. In addition, its high bandwidth-consumption
p oten tial of ten leads to bandwidth-starv ation of other In ternet applications. The
result is p erformance deterioration of these applications and general dissatisfaction
b y other customers of the ISP . Although the global prop ortion of P2P traffic has
reduced tremendously since some y ears no w, it still remains the dominant peak
p erio d upstream traffic in some regions of the w orld [ 207 ]. In the do wnstream di-
rection, real-time entertainmen t services are curren tly dominating, such as Netflix
and Y outub e (on a relativ ely large scale), as w ell as Amazon Video and iT unes (on
a relativ ely smaller scale). Inte rnet video will accoun t for 80 to 90 p ercen t of total
IP traffic b y the y ear 2021 [ 109 ].
5

Chapter 1 In tro duction
1.3 Problem Statement
By reason of the ab o ve men tioned c hallenges, it is clear that there is a p ersisten t
need to devise faster, b etter and more cost-effectiv e wa ys of managing the observ ed
con tin uous traffic gro wth and their related effects. In dealing with this issue, some
ISPs ha v e even resorted to drastic measures, suc h as throttling, prioritizing some
traffic while slo wing do wn others, establishing pre-defined monthly limits and de-
tecting hea vy users who exceed them, in order to either limit their bandwidths or
bill them [ 79 , 217 ]. Suc h measures are rather coun ter-pro ductive and turn to scare
customers a w a y . They further in tensify the c hallenges that ISPs face, in comp eting
with eac h other to win o v er the same group of new customers, while concurren tly
retaining old ones. ISPs are sometimes comp elled to rev erse suc h actions, either
b y self-will [ 36 ] or b y regulatory/court actions against them [ 117 ]. Th us, b etter
approac hes than these are needed. Approac hes that b enefit b oth the ISP and the
customer.
In this thesis, w e iden tify the ISP bac kb one as a ma jor area of in terest and prop ose
t w o nov el approac hes to help impro v e its p erformance. W e use sim ulation studies
and analyses to ev aluate and demonstrate ho w eac h approach con tributes to the
desired goal. In the first approac h, w e prop ose the use of a simple and easy-to-
implemen t service, the Oracle service, whic h effectiv ely helps the ISP win bac k
con trol of a large p ortion of the backbone traffic, and impro v e general net w ork
p erformance, as w ell as end-user exp erience. In the second approac h, w e prop ose a
solution that exploits already a v ailable net work resources to the maxim um p ossible
exten t, in order to optimize traffic flo ws and so, impro v e general p erformance, ev en
on v ery short timescales. The second approac h further helps the ISP to prolong
its upgrade cycles and in so doing, minimize asso ciated upgrade-costs o ver longer
p erio ds, while still ensuring that SLAs and go o d end-user exp erience are main tained
and guaran teed.
1.4 T raffic Management App roaches
ISPs need to plan and budget for exp ected, as well as unexp ected gro wths. Infras-
tructural and op erational c hanges are often in v olv ed. Changes, often first need to
b e planned, then tested (in a lab or a test net w ork) and v alidated, b efore b eing
implemen ted on pro duction netw orks. On the one hand, suc h pro cesses are quite
resource-in tensiv e and time-consuming. On the other hand, most implemen tations
on pro duction net works need to occur in a timely manner, i.e. b efore the effects of
an y iden tified issues (such as rapid gro wth or failure) starts to impact the net w ork’s
p erformance. With regard to traffic gro wth and managemen t c hallenges on bac kb one
net w orks, an effective approac h generally includes clev er capacit y planning.
6

1.4 T raffic Managemen t App roac hes
Capacit y planning is simply a pro cess that ensures enough resources are provisioned
and allo cated to accommo date gro wing demands. The planning pro cess needs to
factor in all imp ortan t parameters. These include close estimates of the assumed
gro wth rates and the traffic demands that the net w ork is exp ected to carry with-
out exp eriencing congestion. The optimal goal remains the accommo dation of all
planned/unplanned gro wths and spik es, while limiting (or completely a v oiding) con-
gestions and failures.
1.4.1 Device Upgrades
As far as pro cessing p o w er and forwarding speeds are concerned, recen t adv ances in
hardw are (c hip and transceive r) tec hnologies [ 148 , 194 ] and soft w are dev elopmen t
are helping man ufacturers build more p o w erful communication devices, e.g. bac k-
b one routers and switc hes, whic h are faster, more efficien t, capable of handling larger
traffic v olumes and transmit at higher line sp eeds [ 150 , 151 , 193 ]. ISPs are taking ad-
v an tage of the adv anced features and capabilities of these devices to redesign their
arc hitectures, re-engineer their infrastructures and simplify/automate administra-
tion and managemen t. With these new devices, they aim to gain more flexibilit y ,
higher efficiencies and b etter o verall performance. These adv anced features and in-
no v ativ e designs ha v e, for example, led to recen t shifts to w ards Soft w are-Defined
Net w orking (SDN), virtualization and cloud-hosted (instead of inhouse-hosted) ser-
vices.
1.4.2 Bandwidth Provisioning
The capacit y of the net work is a measure of the maxim um amoun t of data that
could b e transp orted b et w een lo cations on the netw ork. F or bac kb one net w orks, the
most imp ortan t resource is the link bandwidth. This translates in to ensuring that
bandwidth is sufficien tly (o ver-)pro visioned across all bac kb one links.
A simple approac h often used b y ISPs, is collecting utilization statistics of core
links and upgrading them based on a simple rule of th um b principle, suc h as when
their a v erage utilization attains 50% or some other ISP-determined target. Ho w-
ev er, with this approac h, the ISP is not optimizing on their inv estmen ts, as more
capacit y is often pro visioned than is really necessary . A dditionally , there are still
no guaran tees that links whic h are already o v er-pro visioned using this approac h, are
also pro visioned enough to deal with link and device failures [ 166 , 192 ].
A b etter approac h uses metho dologies that determine bandwidth requiremen ts to
meet SLA goals, while also taking influen tial parameters, suc h as link and device
failures in to consideration. The goal here, is to maintain performance and scalabilit y
at all times, even during failures, while concurren tly minimizing the capacit y that
7

Chapter 1 In tro duction
has to b e o v er-pro visioned. This further satisfies another imp ortan t goal, whic h is
to minimize the o v erall cost asso ciated with o ver-pro visioning.
1.4.3 A dding New Links
Another simple approac h sometimes used b y ISPs, is the addition of new links to
the top ology . This can b e done by ether adding to already connected no des, i.e.,
creating parallel links or b y creating new links where none existed b efore.
• P arallel Links : P arallel links refers to new links that are added b et w een
lo cations that are already directly connected with eac h other. This is often
required when the curren t link(s) ha ve maxed out their a v ailable capacity , suc h
that bandwidth pro visioning can no longer b e p erformed on them. A ddition
of new link(s) to form parallel links, b ecomes a feasible alternativ e to increase
the bandwidth b et ween the t w o lo cations. Although this option has little to
no arc hitectural impact on the existing top ology , it still influences the routing
proto col, b y enabling it tak e adv antage of the added link(s) (bandwidth) to
re-adjust its routing metric, whic h in turn affects the traffic flo w.
• Non-P arallel Links : ISPs can also create completely new links b et w een
lo cations that are not y et directly connected with eac h other. Suc h are referred
to, as non-parallel links. Since this option brings c hanges to b oth the net w ork
arc hitecture and the top ology , the ISP needs to first analyze its impacts on
the net w ork as a whole b efore implemen ting. Prior planning is th us necessary ,
whic h also adds to its complexit y . Ignoring it could lead to un w anted effects.
Both of these men tioned options are usually preceded b y mid to longterm planning
and asso ciated with costs, whic h often also need to b e justified and appro v ed. A
short timescale solution using this metho d is th us quite unlik ely .
1.4.4 Flo w Rerouting
Flo w rerouting is the pro cess of changing the paths that flo ws tak e, either as a
resp onse to c han ges in net w ork conditions or as a means of ac hieving a desired
Qualit y of Service (QoS) goal. A typical scenario in v olv es traffic demands with
flo ws b etw een domains, i.e. flo ws that transit via dedicated ingress links through to
a set of egress links on the ISP’s bac kb one. The demand mo del allows prediction
of ho w c hanging the internal routing impacts the distribution of load on the ISP’s
bac kb one links [ 53 ]. Flo w rerouting is also used to a void b ottlenec ks. The adv an tage
of this approac h is that it could b e used on a smaller (shorter) timescale.
8

1.4 T raffic Managemen t App roac hes
1.4.5 Change of T raffic Matrix
The T raffic Matrix (TM) of a comm unication netw ork is a measure of the total
amoun t of traffic b etw een all p ossible Origin and Destination (OD) pairs (or no des)
of the net w ork. It is an imp ortan t input comp onen t for optimal net w ork design,
capacit y planning and traffic engineering [ 152 , 205 ]. An accurately measured TM is
an imp ortan t and critical to ol, used b y ISPs to predict future traffic trends, detect
anomalies and p erform net w ork optimization.
A c hange of T raffic matrix, as a result of changing where traffic en ters and/or lea v es
a particular domain, is another approac h used to manage/con trol the flo w of traffic
across an ISP bac kb one. Although this is a tec hnically feasible solution, con tractual
agreemen ts and already implemen ted routing p olices migh t need to b e v erified and
adjusted first b efore implemen tation, whic h is a p oten tial hindering factor to a timely
implemen tation.
1.4.6 Flo w Optimization
An IP traffic flo w is a sequence of pac kets of common source that at an y giv en time,
are passing through a common path (or link(s)) to arriv e at a common destination.
It can generally b e iden tified b y means of a 5- to 7-tuple, whic h includes the source
address, the destination address, the source p ort, the destination p ort, the la y er 3
proto col, the class of service and the device (router or switc h) in terface, with all but
the last, b eing attributes of the IP pac k ets.
Flo w optimization uses a v ariet y/combination of approac hes to con trol the flo w of
pac k ets on the net w ork.
Optimization using The Oracle Service
The emergence, rapid gro wth and disruptiv e nature of P2P traffic on bac kb one net-
w orks, coupled with their ability to establish o v erla y net w orks that are completely
agnostic of the underla y net work [ 5 ], p osed h uge managemen t as w ell as capacit y-
planning c hallenges to Pro viders. The traffic o v erhead of P2P systems is relativ ely
high. One reason for this, is the attempt b y p eers to infer net work condition them-
selv es, as a means to impro v e p erformance. The information that P2P no des need,
but can’t accurately infer, is the same information that ISPs already p ossess, but
w on’t publicly share.
In order to limit the disruptiv e nature of P2P traffic and curb their negativ e impact
on bac kb one net w orks, a prop osal to enable collab oration b et w een P2P systems and
the ISP is made [ 4 ]. An approac h based on this prop osal is the Oracle service
[ 8 ]. It is a pro ximit y service hosted b y the ISP and freely offered to P2P no des, to
aid them lo cate and select ’b etter placed’ neigh b ors on the o v erlay net w orks. The
9

Chapter 1 In tro duction
Oracle service acts as the collab orator that ’passes’ ISP information to the p eers in
an uncon v entional but secure manner. By expressing a preference with resp ect to
lo calit y , it helps P2P nodes make better decisions, when selecting p oten tial neigh b ors
or sources to do wnload con ten t from. It do es this b y sorting out and expressing a
preference based on lo calit y , using information originally supplied b y the requesting
p eer. It do es not directly send net w ork-related data to the p eer and th us prev en ts
ISP information from b eing compromised. Confiden tialit y is therefore main tained.
P eers that use the Oracle service b enefit from the ISP’s knowledge of the underla y
net w ork to establish more coherent o v erla y net w orks, ev en tually leading to netw ork
p erformance impro vemen ts and b etter user exp erience. The ISP also b enefits b y
gaining increased influence and con trol o ver this h uge "disruptiv e" constituen t of
traffic flo wing via its (bac kb one) net w ork [ 3 ]. Regaining con trol of a h uge prop ortion
of its traffic increases the ISP’s abilit y to more effectiv ely engineer it, so as to retain
most of the traffic within its o wn AS domain and sa v e on transit costs (if/where
applicable). The ISP can no w also plan b etter and offer b etter services to its other
customers.
W e note here that a similar approac h to the Oracle service, named Pro vider P ortal
for (P2P) Application (P4P) , is prop osed in [ 214 ]. They prop ose a collab oration
platform, in whic h iT r ackers , o wned b y individual ISPs and appT r ackers in P2P
systems, comm unicate and share information to impro v e the p erformance on b oth
sides. ISPs feed their iT rac k ers with netw ork-related information that P2P clien ts
can retriev e through querying using their appT rack er.
There are fundamen tal differences b etw een the Oracle and P4P approac hes, in the
metho d and implemen tation details of the collab oration. In P4P , the ISP collab o-
rates with the P2P user b y passing on net w ork-related information (secrets) to the
p eer. W e argue that this p oses p oten tial risks to the ISP . Since, giving out such
priv ate information, could in extreme cases, b e exploited and used against the ISP .
Our approac h with the Oracle service, offers the same service, but with the added
adv an tage of not needing to rev eal an y ISP-related net w ork secrets to the p eers.
Optimization using Provider-aided Distance Info rmation System (P aDIS)
As usage patterns shift from P2P file-sharing to media consumption, Con tent Dis-
tribution Infrastructures (CDIs), whic h handle media distribution to end-users, are
increasingly b eing c hallenged as w ell. CDIs ha v e to optimize their op erations to
accommo date gro wing d emands, while still guaran teeing optimal user exp erience.
P o ese et al [ 162 ] based their work on the same approac h as the Oracle, to enable
ISP/CDI collab oration, with the ISP offering a similar kind of service to CDIs. The
new service, named Pro vider-aided Distance Information System (P aDIS) ,
is hosted b y the ISP and allo ws collab orating CDIs to obtain needed mapping and
other op erational information related to the ISPs’ infrastructure, without the ISPs
ha ving to rev eal any of their operational secrets.
10

1.5 Summary of Con tributions
Application La y er T raffic Optimization (AL TO)
The AL TO w orking group was created b y the In ternet Engineering T ask F orce
(IETF). Its goal is to merge the differen t optimization prop osals and work out a com-
mon standardized AL TO proto col. So far, the AL TO proto col [ 10 ] and deploymen t
considerations [ 188 ] ha v e b een prop osed in RF C7825 and RF C7971 resp ectiv ely .
Optimization Using Mixed-Integer Programming (MIP)
The often observ ed traffic upsurges and momen tary spik es on bac kb one links, con-
tin ue to pressure ISPs for shorter timescale solutions that are also cost-effectiv e.
With Mixed In teger Programming, optimized metrics for efficien t routing of traffic
flo ws and distribution of load on the net w ork, could b e determined in a matter of
min utes. The adv an tages of this approac h include; its sp eed, exploitation of already
a v ailable resources and the fact that no time-consuming and exp ensiv e ph ysical
top ological c hanges are in v olv ed. More information on this approac h is pro vided in
c hapter 6.
1.5 Summa ry of Contributions
Studies that in v estigate correlations b et w een the o v erla y net w orks formed by P2P
systems and the underla y net w orks of the ISP , sho w that neigh b or-relationships in
the o v erlay net w ork are either randomly [ 5 ] or at most selfishly [ 180 ] formed. This is
in stark con trast to ho w they are formed in the underlay net w ork. F urther analyses
rev eal that a large p ortion of these neigh b or-relationships are formed b etw een p eers
that b elong to differen t ASes, although other p oten tial p eers exist in their same AS
and ev en in their same lo cation.
Based on these foundational w orks and other rep orted findings, w e prop ose a solu-
tion that fosters co op eration b et ween P2P users and ISPs, as w ell as impro v e the
correlation b et ween the o v erla y and the underla y netw orks.
With shifting demand patterns and new trends so far w arran ting different stre am-
lined approac hes to tac kle, what is fundamen tally an old c hallenge, w e prop ose
another approac h that offers a shorter timescale solution and mak es us e of existing
resources to optimize the flo w of traffic on bac kb one net w orks. It emplo ys Mixed-
In teger Programming to determine b est routing costs for optimized traffic flo w.
The follo wing summarizes the main con tributions of this thesis:
11

Chapter 1 In tro duction
The Oracle Service
W e prop ose a new and freely offered service, the Oracle service, whic h is hosted b y
the ISP and helps p eers mak e informed decisions, as to whic h p eers they prefer-
en tially should connect with, when joining the o verla y or whic h p eer to do wnload
from, after a search with results from m ultiple p eers. The ISP kno ws its net w ork
b est and through the Oracle service, can freely offer this information to p eers, in
a form that do es not rev eal in ternal details. That is, details that could render the
net w ork vulnerable to attacks or cause a business disadv an tage. The p eers b enefit
from this service b ecause it sa ves them the need to infer the same net w ork prop er-
ties themselv es [ 47 , 172 ], a pro cess whic h is often m uc h tedious, but less accurate.
Lo calization b y preference is one of the services offered by the Oracle. P eers are able
to preferen tially select neigh b ors that are in the same AS and the same (or nearest
p ossible) lo cation lik e themselv es, based on informed decisions made p ossible by
the Oracle service. In effect, the service helps lo calize the traffic b et w een p eers and
through that, enable the ISP to retain a go o d p ortion of the o v erla y traffic within its
o wn domain. This is a win-win situation, since lo calized traffic impro v es download
resp onse times for the p eers, while also reducing transit costs for the ISP .
Analyses of P eer-to-P eer/ISP Collab o ration
W e conduct pac ket-lev el sim ulations to analyze the prop osal and quan tify the p er-
formance impro v ements for both the P2P user and the ISP .
Compa rative study of traffic effects on different backb one top ologies
W e in vestigate the effects that h uge traffic flo ws generally ha v e on differen t bac k-
b one top ologies. Using a national bac kb one net w ork mo del for reference, we study
ho w three deriv ed top ologies are affected b y the same volume of traffic. All three
top ologies ha ve 12 no des, but differ in the n um b er of their links and ho w these links
are connected. A fullmesh top ology with 66 links and t w o partial-mesh top ologies
with 30 and 20 links resp ectiv ely , are created. Our analyses sho w that the fullmesh
top ology with 66 links is an o verkill, as it p erforms b est, y et in man y cases, its
p erformance remains comparable to those of the partial-mesh top ology with only
30 links. With increased traffic, the top ology with 20 links (the least n um b er of
links) is observ ed to b e the one that is also most affected b y congestion, leading to
p erformance degradations, a phenomenon whic h is not (or only minimally) observ ed
in the other t w o top ologies.
12

1.6 Structural Ov erview
Flo w optimization using Mixed-Integer Programming (MIP)
Understandably , the top ology that has the least num b er of links (i.e. 20 links) and
the least amoun t of total bandwidth of all three top ologies, is also the one exp ected
to offer the least p erformance. Ho w ev er, further analysis of this top ology rev eals that
while some links are suffering from o v er-utilization, others carry little-to-no traffic
at all. W e thus in v estigate if comparativ e gains could still b e attained if the traffic
flo w is engineered differen tly . W e therefore prop ose a metho d that emplo ys Mixed-
In teger Programming to help determine and select optimal flo w paths through the
net w ork. W e carry out further sim ulation studies to assess and compare its effects.
Our findings sho w that comparativ e gains are attainable usin g this metho d.
1.6 Structural Overview
The rest of the thesis is stuctured as follo ws:
Chapter 2 pro vides bac kground information up on whic h the thesis is based. It also
presen ts In ternet trends that are curren tly impacting b oth end-users and Service
Pro viders.
Chapter 3 presen ts structural prop erties of bac kb one net works and the c hallenges
they face with ev er-gro wing traffic. It also presen ts the solutions/discussions that
are helping to tac kle these issues.
Chapter 4 sp ecifically in vestigates the case of P eer-to-P eer as a ma jor bac kb one
traffic con tributor. It describ es the Oracle service and sho ws how it functions as an
enabler for P eer-to-P eer and ISP co op eration. It then presen ts the sim ulations done
in supp ort of this concept and pro vides their results and analyses.
Chapter 5 in v estigates the p erformance of different bac kb one top ologies under the
same traffic conditions. In order to study these effects, the differen t top ologies and
traffic conditions are mo deled using a p opular net w ork sim ulation to ol. It then
presen ts the results obtained from studying the effects of increased traffic and single
link failure on eac h of the top ologies.
Chapter 6 lo oks deep er in to the least p erforming top ology of Chapter 5. It presen ts
our prop osal of using Mixed-In teger Programming to optimize traffic flo w and im-
pro v e the p erformance in this top ology as w ell. The sim ulation results and analyses
that supp ort the feasibilit y of the prop osal are also presen ted.
13

Chapter 1 In tro duction
Chapter 7 summarizes our results and the conclusions dra wn from our findings. It
also discusses and pro vides directions for future researc h.
14

2
Overview of the Internet
In this chapter, we pr esent a gener al overview of the Internet, including b asic c on-
c epts and b ackgr ound information that ar e r elevant to this thesis. W e start with its
structur e and a gener al classific ation of its ent ities (Tiers) and owners (Internet
Servic e Pr oviders). W e next lo ok into the kinds of r elationships/inter c onne ctions
that ISPs have with e ach other, the r outing pr oto c ols they use for c ommunic ation,
including the standar d TCP/IP pr oto c ol suite use d by systems on the Internet. The
differ ent typ es of Servic e Pr oviders ar e also pr esente d, fol lowe d by the differ ent typ es
of servic es and applic ations that they offer to their customers. Some of these appli-
c ations/servic es ar e fundamental to the Internet’s op er ation, while others ar e quite
p opular with end-users. W e c onclude the chapter by outlining some of the c ommon
metrics that ar e use d to assess p erformanc e on the Internet and elab or ate on how
they ar e me asur e d.
The In ternet is a global net w ork of in terconnected autonomous net w orks (or au-
tonomous systems). An A utonomous System (AS) is a group of net w orks under
the same administrativ e con trol. These often also share the same external rout-
ing p olicy . Most ASes are o wned and indep endently operated by In ternet Service
Pro viders (ISP) for profit. There are curren tly o v er 60 thousand registered A u-
tonomous Systems on the In ternet to day [ 33 ]. The In ternet’s size (and con tin uous
gro wth) has b een and con tin ues to b e a topic of main in terest to b oth researc hers
and op erators.
2.1 Internet Structure
Since its inception, the Internet has ev olv ed in to a roughly hierarc hical, but y et a
complex structure of in terconnected net works. Agreemen ts b et w een ISPs, includ-
ing the p olicies that they mak e and implemen t, partly accoun t for the In ternet’s
structural arc hitecture, as w ell as the direction and sp eed of its ev olution.
The v arious ISPs that op erate the Internet can be classified in many differen t w a ys.
A t the AS-lev el, they can b e classified in to one of three ma jor hierarc hical tiers ,
15

Chapter 2 Ov erview of the In ternet
Figure 2.1: T raditional In ternet Structure
dep ending on their role, the size of their AS and their geographical fo otprin t. These
hierarc hies range from Tier1 (top of hierarc hy), o v er Tier2 (middle of hierarc h y) to
Tier3 (b ottom of hierarc h y).
Tier1 (or Global) ISP : Tier1 ISPs manage v ery large netw orks that spread across
m ultiple con tinen ts and large geographical areas. Only a small group of ISPs fall
within this category . Eac h Tier1 ISP connects directly to all the other Tier1 ISPs
as equal partners (settlemen t-free).
Tier2 (or Regional) ISP : Tier2 ISPs are regional in scop e, i.e. they op erate
within a defined geograph y , whic h is less than global. Their geographies are usually
national or con tinen tal, but not global, as it is with Tier1 ISPs.
Tier3 (or A ccess 1 ) ISP : Tier3 ISPs op erate in the last-mile. They op erate at
the edge of the In ternet and pro vide access to businesses and homes. Their scop e
of op eration is geographically limited to to wns/cities, provinces or national b ound-
aries.
In terconnections b etw een ISPs are mainly driv en b y economic incen tives. As a re-
sult, the In ternet’s structure is also ev olving. The traditional structure in Figure
2.1 is ev olving in to the recen t more flatter (traffic-driv en) structure sho wn in Figure
2.2 . The Net w ork Access P oin ts (NAPs), which w ere public facilities where ISPs
connect with eac h other for p eering, ha ve long been replaced by curren t-da y In ter-
net Exc hange P oints (IXPs). ISPs generally establish one of t w o ma jor kinds of
1 A ccess ISP increasingly refers to the role than to the type of ISP , since some Global ISPs and
most Regional ISPs also often ha ve in ternal business units that offer the same line of services.
16

2.1 In ternet Structure
Figure 2.2: Recen t Internet Structure - illustrating dominan t In ternet traffic patterns
[ 130 ]
in terconnection relationship with eac h other, i.e. either a ‘transit’ or a ‘p eering ’
relationship.
A tr ansit relationship in v olv es the pa ymen t of a settlement fee for transport services.
It is usually established b et w een ISPs of differen t tiers, suc h as b et w een a higher-
lev el Tier1 ISP and a lo w er-lev el Tier2 ISP or b et w een a Tier2 ISP and a Tier3
ISP . The lo wer-lev el Tier2 or Tier3 ISP pa ys the higher-lev el Tier1 or Tier2 ISP ,
resp ectiv ely , to carry its traffic to the rest of the In ternet. The amoun t of fees paid
is usually prop ortional to the v olume of transit traffic that is transp orted, i.e., the
higher the v olume of traffic, the higher the fees.
In a p e ering relationship, t w o ISPs agree to a settlemen t-free exc hange of routing
information and traffic. They b oth share the cost for the connection(s), but none
pa ys the other for the v olume of traffic exc hanged. This kind of relationship is
common b et ween ISPs of the same category , e.g b et w een Tier1 ISPs or b et w een
Tier2 ISPs in the same (or neigh b oring) regions. The main driv er b ehind this kind
of relationship, esp ecially b et w een non-Tier1 ISPs, is to a v oid or minimize the cost
for transit.
Generally , the normal practice is suc h that Tier2 ISPs in the middle of the hierarc h y ,
purc hase transit services from Tier1 ISPs and also offer transit services to ev en lo w er
Tier3 ISPs. It is also b ecoming more common for Tier3 ISPs to purc hase transit
services directly from Tier1 ISPs. T o manage transit costs, b oth Tier2 and Tier3
ISPs resp ectiv ely also en ter in to p eering relationships with other ISPs of the same
tier. It should also b e noted here that, just like Tier3 ISPs, some Tier1 and Tier2
ISPs also offer access services to businesses and homes.
17

Chapter 2 Ov erview of the In ternet
T o in terconnect, each ISP first needs to register its AS with the appropriate Re-
gional In ternet Registry (RIR). It then obtains a public A utonomous System Num-
b er (ASN) and can then use it to enable comm unication an d routing information
exc hange b etw een itself and its p eering/transit partners. Th us, while eac h ISP can
indep enden tly decide on ho w routing within its o wn AS should o ccur, in o der to
in terconnect with other ASes and pro vide a global reac h to its customers, it is com-
p elled to adhere to standardized guidelines that stipulate the use of ASN and an
Exterior Gatew a y Proto col (EGP) to enable an in terconnection. Irresp ective of the
AS that remote systems and users b elong to, end-to-end comm unication and data
exc hange b etw een them is made p ossible through this means.
2.1.1 Edge and Backb one Net w o rks
Net w orks, suc h as the Internet, ha v e t w o ma jor (ph ysical) elemen ts of great sig-
nificance: i) the links through whic h data flo ws and ii) the switc hes/routers that
con trol the flo w of data on these links. Many differen t kinds of media can b e used
to establish these links, ranging from wireless (suc h as Radio F requencies (RF s) in
LANs and satellites across larger geographies) to wired (suc h as copp er, coax and
Fib er cables for LANs and W ANs). The t yp e of media used, usually dep ends on the
requiremen ts for that segmen t of the net w ork. A t the edge, i.e. at the en try p oin t
of the net w ork, access to man y differen t customers is needed, th us the quan tit y and
v ariet y of a v ailable en try p oints is comparativ ely more imp ortan t. In the core of the
net w ork, the capacit y to handle aggregated traffic from the edge, is more imp ortan t.
Th us, sp eed and capacit y are more imp ortan t.
T aking these in to consideration, the In ternet can also b e classified in to edge versus
core net w orks. That is, a customer-facing edge, consisting of a large n um b er and
v ariet y of access links of small-sized to medium-sized bandwidth and a pro vider-
managed core, consisting of a m uc h smaller n um b er of aggregated high-sp eed links
of m uc h larger bandwidths/capacities.
• Edge Net w orks : An edge net w ork is a net w ork that is lo cated on the p e-
riphery of an ISP’s net w ork. It demarcates the en try p oin t for traffic flowing
from customers and p eers net w orks, to/through the ISP’s o wn net w ork. Edge
net w orks can generally b e classified in 2 main categories; ac c ess net w ork that
carries traffic from/to home and business customers and p eering in terconnec-
tions that carry traffic from/to other ISPs.
• Bac kb one Net w orks : Backbone netw orks carry the bulk of all the traffic
that tra v erses the In ternet. They are c haracterized b y v ery large capacit y
high-sp eed links and high-end bac kb one routers that can handle large n um-
b ers of aggregated flo ws of differen t classes. Their main design and op erational
goals include; high a v ailabilit y , scalabilit y and resiliency when faced with link
and/or device failures. Backbone netw orks are therefore exp ected to p ossess
18

2.2 Service Pro viders
m ultiple paths b etw een an y t w o P oPs, in order to accommo date and mitigate
suc h failures. That is, failures should not negativ ely impacting p erformance,
e.g through additional dela ys or pac k et loss. Since distances b et w een in tercon-
nected P oPs could b e quite large, re-routing during failure, could mean taking
an ev en longer path, which turns to increase the dela y that pac k ets exp erience.
Rerouting is th us done as a last resort. A b etter approac h that is commonly
used to a v oid this kind of issue, is to run m ultiple (and often disjoin t) links
b et ween t w o adjacen t P oPs, so that in case of a link failure, the traffic load
could ev enly b e shared among the remaining op erating links.
2.1.2 Internet Exchange P oints
An In ternet Exc hange Poin t (IXP) is a lo cation where differen t autonomous net w orks
ph ysically in terconnect with eac h other to exc hange traffic. They con tribute a lot
to the structure of the In ternet and pla y a significan t/facilitating role in p eering
b et ween ISPs, CDNs and Pro viders of other In ternet services [ 24 ]. Ground-breaking
and insigh tful analyses of their traffic is offered in [ 2 ]. IXPs are usually disp ersed
across a coun try or region. This creates pro ximal exc hange p oin ts for lo cal traffic
and th us eliminate the need to exc hange lo cal traffic in further a wa y or ev en o v ersea
lo cations. IXPs also create a unique lo cation to host other essen tial Internet services,
suc h as DNS, W eb cac hes, time servers, ro ot serv er mirrors, etc, b ecause of the
pro ximit y to the connected net w orks and users. As a result, IXPs enable faster
switc hing/routing sp eeds, faster access to conten t and hosted services, b etter user
exp erience and cost reductions for the participating ISPs. They also pro vide the
appropriate lo cation to install v antage points in the In ternet [ 25 ].
2.2 Service Providers
The In ternet is basically a kind of service mark etplace, where service providers and
service consumers come together to resp ectiv ely sell and buy service-commo dities.
The nature of suc h commo dit y could b e commercial, educational, for business, for
pleasure or man y other options. A fundamen tal requiremen t for the consumer, whic h
is also the most common service offered b y an ISP , is universal access to the whole
In ternet via the lo cal ISP’s o wn autonomous net work.
Despite global co v erage by some v ery large ISPs, there is none that can pro vide
univ ersal end-to-end access without collab orating with at least one or more of the
other ISPs. These collab orations are strategically business in nature and result in
con tractual agreemen ts that state the conditions and price for exc hanging traffic
b et ween their ASes.
A Service Pro vider (SP) is a compan y that offers sp ecific services to businesses,
organizations or individuals on the In ternet for a fee. Service Pro viders are often
19

Chapter 2 Ov erview of the In ternet
classified b y the t yp e of service(s) that they offer. These include; In ternet access,
hosting, con ten t distribution, cloud services, video on demand and many others.
Some SPs offer a com bination of these services, while others offer only a single
t yp e and are classified by that one t yp e alone. F or example, a compan y that offers
In ternet access services, is generally referred to as an In ternet Service Pro vider
( ISP ). Ho w ever, big ISPs often offer one or more of the other services as w ell.
Th us, based on the sp ecific service offering, Service Pro viders can also b e categorized
as follo ws:
• Net w ork Service Pro vider (NSP) : NSPs are businesses that offer pac k et-
forw arding services and In ternet Proto col (IP) services on the In ternet. They
include ac c ess pr oviders , who pro vide in ternet access services to businesses
and individuals and b ackb one pr oviders , who op erate large global net works and
pro vide transit services to other (smaller) pro viders, ev en across long distances.
NSPs are resp onsible for creating and managing In ternet connectivities.
• Application Service Pro vider (ASP) : An ASP offers upp er la yer appli-
cations or soft w ares, suc h as email, instan t messaging or w eb-based training,
whic h require In ternet access as a primary condition for use.
• Hosting Service Pro vider (HSP) : HSPs offer w eb-hosing services o v er the
In ternet.
• Con ten t Distribution Service Pro vider (CDSP) : A CDSP is a pro vider
who offers sp eedy deliv ery/distribution of w eb and ric h media con ten ts to end
users. They build Con ten t Deliv ery Netw orks (CDNs), whic h are o v erla y net-
w orks op erating on top of the IP underla y net w ork, allo wing them to distribute
con ten t with no need to manage the underlay themselv es.
• Con ten t/Information Pro vider : They are o wners of the con ten t or infor-
mation that is distributed/transp orted b y the CDSP or the NSP resp ectiv ely ,
to the end users. The t yp es of con ten t or information range from w eb p ortals
to video/audio data and also services suc h as Go ogle searc h and wikip edia.
• Cloud Service Pro vider (CSP) : Cloud Service Pro viders are businesses
that offer net w ork, infrastructure or application/soft w are services in the cloud.
These services are accessible to subscrib ed customers only via the net w ork.
Ob viously , no single Pro vider can alone offer all the services that all customers
need. Ho w ev er, through net work in terconnections, service collab orations and busi-
ness partnerships, a large n um b er of these services can b e transparen tly offered to
requesting customers, while the details of an y inv olv ed collab orations are k ept pri-
v ate. Since the system offering a particular service and the consumers of that service
are often not in the same lo cation or b elong to the same ISP , the net w ork pla ys the
fundamen tal role of b eing the medium, through whic h comm unications and infor-
mation exc hange b etw een these systems happ en. End-systems on the In ternet that
20

2.3 The TCP/IP Proto col Suite
wish to comm unicate with other end systems, need to use standardized proto cols.
TCP/IP is the suite of proto cols that has b een appro v ed and standardized for this
purp ose.
2.3 The TCP/IP Proto col Suite
Figure 2.3: TCP-IP Proto col Suite with clien t-Serv er in teraction
The In ternet generally op erates via implementation of standardized protocols. A
proto col is a set of syn tactic and semantic rules or procedures that gov ern ho w
comm unications o ccur. Computers and all other devices on the In ternet, use suc h
proto cols to comm unicate and exc hange data with eac h other.
The T ransmission Con trol Proto col (TCP) and the In ternet Proto col (IP) are t wo
outstanding proto cols, among others that b elong to the suite of proto cols, collec-
tiv ely kno wn as the TCP/IP proto col suite [ 129 ]. Figure 2.3 illustrates the
TCP/IP proto col suite and its use in a t ypical communication betw een a clien t and
a serv er.
The TCP/IP proto col suite consists of fiv e lay ers 2 , which together pro vide in tercon-
nection and comm unication services b et w een devices [ 65 ] [ 19 ] [ 51 ]. They define the
2 TCP/IP is traditionally represen ted b y a 4-la yer model, i.e. Application, T ransp ort, In ternet
and Net work In terface la yers. Ho w ev er, modern literature increasingly uses an up dated 5-
la yer model, with the Network Interfac e La y er b eing split into the Link and Physic al lay ers
21

Chapter 2 Ov erview of the In ternet
syn tax and formats of messages, e.g. requests and resp onses, that are exc hanged b e-
t w een devices and also sp ecify ho w these handle errors, should they o ccur [ 51 ]. The
main purp ose of TCP/IP is to enable devices, which are usually differen t b y v endor,
hardw are, arc hitecture and purp ose, to comm unicate with eac h other, despite these
differences.
Application La y er: This is the topmost la yer and the la y er at whic h an application
program in teracts with the net work to send and receiv e data. Application la y er
proto cols address the formatting of applications and the commands/resp onses that
the systems offering them m ust supp ort.
T ransp ort La y er: The T ransp ort la y er pro vides transp ort services to the Appli-
cation la y er. It ensures that data passed do wn to it from the Application la y er is
transfered appropriately to the in tended destination and vice v ersa. T w o common
proto cols used to accomplish these tasks are the T ransmission Con trol Proto col
(TCP) and the User Datagram Proto col (UDP). TCP is connection-orien ted and
is used to pro vide reliable transfer b etw een source and destination end-systems. On
the other hand, UDP is connectionless and is used to pro vide simple (and unreli-
able) data transfer b et ween end-systems. T w o additional transp ort-la y er proto cols
that are relativ ely new er, but less used, are the Datagram Congestion Con trol
Proto col (DCCP), sp ecified in RF C 4340 [ 126 ] and the Stream Con trol T rans-
mission Proto col (SCTP), sp ecified in RF C 4960 [ 204 ], resp ectiv ely .
Net w ork La y er: This la yer pro vides addressing and routing services to the upp er
T ransp ort lay er. The main and only significan t proto col at this la y er is the Internet
Proto col (IP). Segmen ts received from the T ransp ort la y er are encapsulated in to IP
pac k ets, eac h with a header that contains both source and destination addresses,
as w ell as other imp ortant information. In termediate systems suc h as routers, use
these information to determine ho w to forw ard the pack et to its next hop. This is
rep eated on a hop-b y-hop basis, until the pac k et’s final destination. There are t w o
v ersions of the IP proto col, the older 32-bit IP v ersion 4 (IPv4) and the newer
128-bit IP v ersion 6 (IPv6). IPv6 w as created to address some short-comings of
IPv4, suc h as address-shortages and securit y .
Link La y er: The link lay er pro vides ph ysical addressing, framing and error detec-
tion services. It encapsulates IP pac k ets in to frames with hardw are (link) addresses
that enable forw arding across the lo cal link.
Ph ysical La y er: This is the lo w est la y er and that at whic h the ph ysical transmission
medium exists. F rames from the Link la y er are con v erted in to ra w bits and then
transmitted o v er a comm unication c hannel on a lo cal medium. T ypical media include
T wisted P air Copp er (TP Cu) and Fib er cables.
resp ectiv ely , and the Internet la yer renamed to the Network la y er. Ov erall, b oth mo dels contain
the exact same functions
22

2.4 In ternet Applications and Services
2.4 Internet Applications and Services
In the early da ys of the In ternet, the service sp ectrum w as quite meager. It consisted
only of a few services, i.e. remote login, remote file access and electronic mail.
Ho w ever, with time and tec hnological adv ancements, man y others ha v e b een added,
a go o d n um b er of whic h are very popu lar and in widespread use to da y .
Most end-users p erceiv e the Internet primarily through the set of applications and
services it offers them. Although some of the implemen ted proto cols are quite com-
plex, the use of Graphical User In terfaces (GUI) b y most applications successfully
hides their details and complexities from the end-users. Therefore, to effectively use
their services, end-users neither need to understand the details of their proto cols
nor kno w ho w they use the underlying net w ork for successful end-to-end comm uni-
cation.
Net w ork applications generally use tw o kinds of in teraction mo dels; the Clien t-Serv er
mo del and the P eer-to-P eer mo del.
2.4.1 Client-Server Application Mo del
In a Clien t-Serv er application mo del, the tasks p erformed b y the service as a whole,
are divided b et ween a server and a client , with the serv er pro viding some t yp e of
service to the clien t.
A server is generally an application program that offers a particular set of services
o v er the net w ork to requesting clien ts. It waits for requests at a w ell known port
reserv ed for the service, pro cesses them when they arriv e, sends back the resp onse
to the clien t, then go bac k to w ait for the next request. The sp eed at whic h these
happ en, defines the efficiency and p erformance of the servicing pro cess.
A client is an application program that requests for and mak es use of the services
offered b y the serv er. A clien t that needs to use a service, e.g. do wnload a file, first
comp oses a request, sends it to the serv er and then waits for the response from the
serv er. Figure 2.3 illustrates ho w such a request is created b y a clien t and forw arded
across the net w ork onto a serv er. The request is sen t to the serv er via a well-kno wn
(reserv ed) p ort. The serv er accepts the request (if it is v alid), creates a resp onse
and sends it to the clien t.
2.4.2 P eer-to-P eer (P2P) Application Mo del
Net w ork applications that are based on the P2P mo del, are designed to b e more
distributed in their functioning. There are no single serv ers that solely op erate
as suc h. The functions of the serv er (and of the clien t) are distributed among
all participating p eers, i.e. eac h p eer can sim ultaneously act as b oth a clien t and
23

Chapter 2 Ov erview of the In ternet
a serv er. A p eer can th us send its request for a service to other p eers and can
concurren tly also resp ond to requests coming from other p eers. If a p eer receiv es a
request that it can’t directly resp ond to, it simply forwards it on to others (neigh b ors)
that lik ely can. This therefore eliminates the need for a cen tral serv er.
The P2P mo del shares some similarities with the client-server mo del describ ed in
section 2.4.1 ab o ve, since its tasks are also distributed b et w een clien ts and serv ers,
just lik e in the traditional sense. Ho w ev er, they also ha v e ma jor differences, in that,
the functions of the serv er are no longer on a dedicated cen tralized system, but are
replicated on all activ e p eers of the o v erlay net w ork.
P2P applications are t ypically used for file-sharing, m ultimedia streaming, telephon y
and gaming.
2.4.3 P opula r Application Services
Common services with applications based on either the clien t-serv er mo del or P2P
mo del include:
Domian Name System (DNS): DNS [ 143 ] is an In ternet naming service, u sed to
con v ert IP addresses into easily recallable high-lev el names and vice v ersa. Mac hines
on the In ternet are normally iden tified by their unique IP address. Human users
w ould therefore need to memorize millions of IP addresses to b e able to connect to
them, whic h is quite a difficult thing to do. T o remo ve this difficult y , DNS serv ers
are used to con v ert these IP addresses in to names that h umans can easily memorize
and recall.
W orld Wide W eb (WWW): WWW (or simply ‘The W eb’) [ 16 ] is the most
p opular and most widely used service on the In ternet. In fact, most users tak e it
to b e the In ternet itself, although it is only one of man y services that the In ternet
offers.
The w eb is a collection of distributed resources (do cumen ts and services) that are
scattered across the In ternet, but link ed together via h yp ertext links. Its three main
comp onen ts are the w eb serv er, the w eb client and the transfer proto col that b oth
the serv er and clien t use for interaction.
• The w eb serv er is where all the resources are stored. The Unive rsal Re-
source Iden tifier (URI) is a string of c haracters that are used to iden tify these
resources. The Uniform Resource Lo cator (URL) is a unique name (or w eb
address) that is used to sp ecify the exact lo cation of a resource on the net w ork.
• The w eb clien t is an application installed on a lo cal system and used to access,
do wnload and displa y resources that are stored on the web serv er. Bro wsers
suc h as Firefo x, Go ogle Chrome, In ternet Explorer, Safari and man y others
are examples of w eb clien ts.
24

2.4 In ternet Applications and Services
• The Hyp ertext T ransfer Proto col (HTTP) [ 57 ] is an application-lev el
proto col for distributed, collab orativ e, h yp ermedia information systems [ 56 ].
W eb serv ers and clients use it to comm unicate with eac h other.
T aking the URL ‘http://www.example.c om/example.html ’ as an example, a web
clien t suc h as the Firefox bro wser, uses the application transfer proto col ‘HTTP’ to
establish a connection with the w eb serv er named ‘www.example.com ’, in order to
access, do wnload and displa y the URI ’example.h tml’ and all its em b edded ob jects
on the lo cal mac hine.
Electronic Mail (email): Email is a p opular In ternet-based metho d of exc hanging
messages (mails) via the use of electronic devices, such as computers, tablets and
smartphones. The delivery service is based on the Simple Mail T ransp ort Proto col
(SMTP) [ 122 ]. Emailing is very fast, with deliv ery o ccurring within seconds and
without the need of h uman in terven tion. The normal (p ostal) mail deliv ery system
requires direct h uman in volv emen t and dep ending on the distance/lo cation, could
require a couple of da ys for the deliv ery to b e completed. The p ostal metho d of mail
deliv ery is also kno wn as “snail mail ”, referring to its slo w deliv ery sp eed, compared
to that of emails. Initially , email allow ed users to exc hange only short text messages,
but with time, it has evolv ed to allo w m uc h longer messages, including em b edded
ob jects, suc h as images and sound.
File T ransfer: File transfer in v olv es the cop ying of computer files from one machine
on to another. This service is also based on the clien t/server model. The clien t
sends a request to the serv er, asking for the file and starts do wnloading a cop y , if
the serv er ac knowledges. An Internet standard-based protocol, the File T ransfer
Proto col (FTP) [ 164 ] is used to deliv er the file from one end-system on to another.
Other p opular proto cols that are used to transfer files include; FTP Secure [ 64 , 88 ],
T rivial File T ransfer Proto col (TFTP) [ 181 ], HTTP , HTTP S [ 170 , 171 ] and Secure
cop y (SCP) [ 200 ], whic h is based on Secure Shell (SSH) [ 215 ].
Remote Login: The remote login service offers the abilit y for a user to log in to a
computer system, as an authorized user, while not b eing physically presen t in the
same lo cation as the system. The three most p opular proto cols for remote login
are rlogin [ 118 ], telnet [ 165 ] and Secure Shell (SSH). With rlogin and telnet, all ex-
c hanges b etw een the lo cal and remote systems are sen t in clear text. These are less
secure as it p oses the risk of the comm unication b eing ea vesdropped without detec-
tion. Mean while, SSH o v ercomes these shortcomings b y pro viding options for strong
authen tication and strong encryption that protect the comm unication’s securit y and
in tegrit y .
File Sharing: File sharing is the pro cess of offering access to digitally stored re-
sources using an appropriate distribution proto col. The stored resource could b e
do cumen ts, eb o oks, computer programs, graphics, as w ell as audio and video files.
On the In ternet, this distribution could o ccur either via do wnload through a hyper-
link, do wnload from a file hosting serv er or using a file sharing P2P application. A
25

Chapter 2 Ov erview of the In ternet
do wnside of file sharing is the imminen t risk of b eing infected b y viruses, adw ares
and sp yw ares. These often get installed on the do wnloading computer without the
user kno wing.
Media Streaming Services: Media streaming in v olv es the transmission of m ulti-
media data (audio or video), from a source (server) system on to a destination system
(or pla y er) for immediate consumption. The original data is compressed and sen t
in con tin uous streams that get pla y ed immediately up on arriv al. The user do es not
ha v e to first sav e the data b efore pla ying it.
Online Gaming Services: Online gaming refers to the act of playing video or role-
pla ying games either partially or fully via the In ternet or another net work. Online
games can b e classified in to different categories. Bro wser games are simple games
that can b e pla y ed using a w eb bro wser. With the supp ort of w eb-based graphic
enhancers, such as Ja v a and Flash, more complex games are also b eing dev elop ed
for the bro wser. Real Time Strategy (R TS) games , suc h as Star cr aft and A ge of
Empir es , hav e nativ e In ternet supp ort that enables connected play ers from all o v er
the w orld to pla y with/against each other.
Cloud Storage: Cloud storage is a cloud computing mo del in whic h data is stored
on remote serv ers accessed from the in ternet or “cloud” . It is op erated, managed
and main tained b y a cloud storage service p ro vider. The service runs on storage
serv ers that are built using virtualization tec hniques.
2.5 P ack et F o rw a rding
In the history of the In ternet, comp etitive metho ds ha v e b een dev elop ed for the
effectiv e forw arding of IP pac k ets. Eac h metho d differs from the other in one w a y or
another. New er metho ds are dev elop ed to o v ercome dra wbac ks of already existing
metho ds. Older metho ds are upgraded to address new er requiremen ts, suc h as the
in tro duction of IPv6. The main goal of the v arious metho ds is generally to address
existing issues in particular scenarios or simply to offer alternativ es that emplo y b et-
ter metrics for impro v ed conv ergence, b etter scalabilit y and b etter p erformance.
Generally , IP forw arding can b e done b y means of routing or more recently b y means
of lab el switc hing.
2.5.1 IP Routing
IP routing refers to the pro cess of forw arding IP pac k ets along a determined path
from their resp ectiv e sources to their intended destinations. On the In ternet, dedi-
cated devices (routers) that comm unicate with eac h other to exc hange connectivit y
and link qualit y information, are used as the primary pac k et forw arders. Routers
use routing proto cols to adv ertise their lo cal subnets to their neigh b ors and receiv e
26

2.5 P ac ket F orw arding
the same ab out remote subnets from their neighbors. If more than one route exists
to a particular subnet, the b est one is selected through the routing proto col’s routing
algorithms, then added to its routing table for use when forw arding pac k ets. In case
of c hanges in the top ology , e.g. due to link or device failure, b y whic h activ e routes
b ecome una v ailable, the routers react by adv ertising these to their neigh b ors and
then select the next b est route as a replacemen t. After suc h a c hange o ccurs, the
time it tak es for all routers in the net w ork to settle do wn with the next b est route
is referred to as the con v ergence time. The quic kness with whic h a routing pro-
to col con verges is an important criteria when selecting p oten tial routing proto cols
for bac kb on e net w orks. Other imp ortan t criteria include supp ort for summarization
and the abilit y to scale prop erly in v ery large en vironments.
Static versus Dynamic Routing
Routing tables can generally b e p opulated using three distinct metho ds; (i) directly
connected routes that are automatically added, (ii) static routes that are man ually
added and (iii) dynamic routes learned and automatically added b y dynamic routing
proto cols.
A dministrators who w an t to determine the exact paths that pac k ets should follo w
usually accomplish this b y man ually adding static routes to the routing table. Static
routes are sometimes undesirable in certain en vironmen ts. This can only b e b ene-
ficial on m uc h smaller net w orks with just a few no des, i.e. where static routes are
predictable and manageable. A main disadv an tage is that static routing requires h u-
man in terv ention to execute and/or appropriately respond to c hanges and up dates,
e.g. during a main tenance or when there are failures that affect the top ology . Static
routes (whic h are often man ually managed) are th us impractical for use in larger
and more dynamic en vironmen ts, where their lac k of scalabilit y could easily b ecome
a ma jor issue of great concern.
On the other hand, dynamic routing uses dynamic pro cesses to learn ab out and
add routes to the routing table. Dynamic routing do es not dep end on external
in terv ention and is designed to resp ond automatically (and th us faster), whenev er
there are top ological c hanges. As a result, dynamic routing proto cols are widely
used on the public In ternet, as w ell as within priv ate en terprise net w orks.
Despite the man y disadv an tages of statically configured routes, they are still in
limited use to da y , e.g. as default routes.
T yp es of Dynamic Routing Proto cols
Classification of dynamic routing proto cols can also b e based on the kind of algo-
rithms they use to carry out their routing functions. The most common of these
include;
27

Chapter 2 Ov erview of the In ternet
• Distance-V ector routing proto col: Distance v ector routing proto cols use
t w o parameters, the distance (e.g. n um b er of hops to reac h the destination)
and a v ector (direction) to determine the route. The num b er of hops is actually
the n um b er of la y er 3 devices (routers) that the pac k et tra v els through to get
to its destination. With this metho d, the router only kno ws the distance (or
metric) to get to a remote net w ork and the vector (path or in terface) to use to
get there. Routers using this metho d do not ha v e an actual map of the net w ork
top ology . They rely on p erio dic exc hanges with their neigh b ors to main tain a
curren t top ology . In terior Gatew a y Routing Proto col (IGRP) [ 191 ], as w ell as
Routing Information Proto col (RIP) [ 84 ], which is one of the earliest routing
proto cols, are b oth based on this approac h.
• A dv anced Distance-V ector routing proto col: A dv anced distance-v ector
(or balanced h ybrid) is a Distance V ector routing proto col with adv anced fea-
tures that o v ercome some of the limitations of the original distance-vector
proto col. A t ypical example is the Enhanced In terior Gatew a y Routing Proto-
col (EIGRP) [ 177 ], which un til 2013, w as a Cisco proprietary proto col. In 2013
Cisco handed it o v er to the In ternet Engineering T ask F orce (IETF) for public
release as an RF C (RF C 7868) to enable implemen tation b y other v endors as
w ell. A ma jor difference b et w een this proto col and the other D V proto cols
that use only the n um b er of router hops as metric, is that, it integrates better
net w ork features, suc h as smallest bandwidth on the path and accum ulated
dela y from the pac ket source to the final destination, in calculating its metrics.
• Link-state routing proto col: The link-state routing approac h uses the
Shortest P ath First (SPF) algorithm [ 62 ] to compute b est paths to all sec-
tions of the net w ork. Each router in the top ology has a complete map of the
whole top ology .
Op en Short P ath First (OSPF) [ 145 ] and In termediate System to In terme-
diate System (IS-IS) [ 156 ] are t w o examples of link-state routing proto cols.
Link-state routers exc hange link-state messages with one another to main tain
and up date their link-state databases (LSDBs). Eac h router constructs and
main tains its o wn copy of the LSDB, whic h pro vides a complete top ological
view of the whole net w ork, from that router’s p ersp ectiv e. The routers use the
Shortest P ath First (SPF) algorithm to determine the b est path to all subnets
on the net w ork. The cost asso ciated with the individual links (also kno wn as
the link w eigh ts) are used for these calculations. The smaller the total cost,
the b etter the path and the more traffic it attracts. Th us, link w eigh ts are
also used to express preference via sp ecific paths through the net w ork.
• P ath v ector routing proto col: The path v ector routing approac h exc hanges
information ab out the existence of net w orks and subnets and the path that
needs to b e tak en to reac h them. The path information pro vides the b est path
to reac h the remote net work and prev en ts routing lo ops. It is, to an exten t,
similar to the distance v ector approac h in that it also do es not pro vide a full
28

2.5 P ac ket F orw arding
top ological view from the p ersp ectiv e of the individual routers. Ho w ever, it
also differs from it, b y the additional path information that it pro vides. BGP
[ 169 ] is a P ath V ector routing proto col.
Intradomain versus Interdomain Routing
There are generally t w o ma jor categories of IP routing proto cols; those that op erate
within an A utonomous System and those that op erate b et w een differen t A utonomous
Systems.
Both ho w ev er share the same fundamen tal design goals, i.e. learning ab out routes,
c ho osing the b est route if m ultiple comp eting routes exist for the same destination
net w ork and conv erging whenev er there is a c hange in the net work topology .
• In tradomain Routing: Intradomain routing protocols are designed for use
within an AS. These proto cols offer differen t features and trade-offs, ev entually
leading to a div ersit y of routing proto cols. Since routers within an AS are under
the full con trol of their ISP , the ISP can freely select the routing proto col that
offers the b est trade-offs or whic h b est meets its needs. T rade-offs include, ease
of implemen tation, adaptabilit y , amoun t of con trol traffic, con v ergence sp eed,
etc. OSPF and IS-IS are the t wo most common in tradomain routing proto cols
used b y ISPs. Other in tradomain routing proto cols include, RIP , IGRP and
EIGRP .
• In terdomain Routing: In terdomain routing is designed for use b et we en
ASes. Routers b elonging to differen t ASes exc hange their routing information
using in terdomain routing proto cols. BGP is the only interdomain routing
proto col in use to da y on the In ternet.
Although in terdomain routing proto cols, suc h as BGP and in tradomain routing
proto cols, suc h as OSPF and IS-IS, share similar design goals, their main emphases
are quite differen t. BGP’s main goals are global reac habilit y and scalabilit y . Its
priorit y is to ensure that all In ternet routers learn ab out all public IP address prefixes
that are reac hable via the In ternet. It th us has to deal with a relativ ely h uge routing
table size, compared to the relativ ely smaller table sizes that in tradomain routing
proto cols ha ve to deal with. The curren t size of the BGP routing table stands at
ab o ve 750,000 [ 96 ]. This is quite a h uge n um b er that w arran ts scalabilit y , as more
and more prefixes are b eing added, due to con tin uous gro wth. At the momen t, BGP
is the most suitable routing proto col that effectiv ely handles the exc hange of suc h
h uge n umbers of routes b et ween ASes, as is required on the global In ternet.
29

Chapter 2 Ov erview of the In ternet
Multipath Routing
Multipath routing is a feature that enables m ultiple paths in the net w ork to b e
used when forw arding pac k ets b et ween a giv en source and a giv en destination. The
distribution of traffic across m ultiple paths pro vides b etter load and resource sharing,
whic h in turn, improv e the o v erall p erformance of the netw ork. There are t w o t yp es
of m ultipath routing sc hemes:
• Equal-Cost Multipath routing (ECMP) forw ards pac k ets to a single des-
tination o v er multiple “best paths” of equal metric v alue (cost). It is a p er-hop
decision that is limited to a single router. V arious routing proto cols, includ-
ing OSPF and IS-IS explicitly allo w ECMP routing. A general discussion on
equal-cost m ultipath routing can b e found in RF C 2991 [ 202 ].
• Unequal-Cost Multipath routing o ccurs when forw arding to a destination
is done o v er multiple paths of differen t metric v alues (costs). In this case, a
v ariance v alue is used to indicate/limit the range of considered metric v alues.
An imp ortan t condition for this feature is ensuring that the routes via the
alternate paths are lo op-free. IGRP and EIGRP are t w o intradomain routing
proto cols capable of carrying out this c heck. They th us supp ort unequal-cost
m ultipath routing, in addition to their supp ort for equal-cost m ultipath rout-
ing. The in terdomain routing proto col, BGP (or eBGP to be more sp ecific),
also supp orts unequal-cost m ultipath routing. BGP ensures that its routes are
lo op-free, b y chec king all routes receiv ed from external ASes, to see if its o wn
AS n um b er is in the AS_P A TH attribute and discarding them if there is a
matc h.
Corresp onding to the ab o v e routing schemes, equal-cost and unequal-cost load-
balancing can resp ectiv ely b e ac hieved when they are used. With equal-cost load-
balancing, the load is shared equally b et w een all paths. In the case of unequal-cost
load-balancing, the amoun t of traffic sent across a particular path is in v ersely pro-
p ortional to the path’s metric v alue.
2.5.2 Multip roto col Lab el Switching (MPLS)
MPLS [ 175 ] is an efficien t metho d of forw arding pac kets betw een net w orking no des,
using short, unstructured and fixed-length lab els instead of the con v entional longest
prefix matc h algorithm. The goal of MPLS is to pro vide b etter QoS to connection
orien ted services, supp ort traffic managemen t that improv es net w ork throughput
and retain IP-based net w orking flexibility [ 185 ]. MPLS allo ws Service Pro vides to
connect man y differen t customers on to the same IP net w ork but use lab el switching
to k eep their IP traffic separated from eac h other.
In con v en tional IP net w orks, pac k ets that are forwarded across m ultiple routing
no des undergo a substan tial amoun t of pro cessing dela y , as eac h router first extracts
30

2.6 Qualit y of Service (QoS)
the la y er-3 header to get the destination address, then lo oks it up in its routing table
and finally p erforms the longest prefix matc h algorithm to determine the next hop
address. With lab el switc hing, the extraction is done only once, in the b eginning
and then mapp ed on to a v alue called the lab el. After a lab el is assigned, a short
lab el header is added in fron t of the original la y er-3 header and forw arded across the
net w ork as part of the pac k et. Subsequen t MPLS no des no longer need to rep eat
this analysis. All they ha v e to do, is simply sw ap the lab els accordingly and then
forw ard them based on their F orwar ding Equivalenc e Class (FEC) . All headers that
map on to the same lab el use the same next hop.
The MPLS forw arding table lo okup pro cess is b oth less complicated and fast b ecause
of the use of unstructured and fixed length lab els and the a v oidance of the prefix
matc hing o v erhead.
2.6 Qualit y of Service (QoS)
The bandwidth aggregate at the edge of the net w ork is often m uc h larger than that of
the bac kb one link that carries the aggregate traffic. When m uc h more traffic arriv es
at the router’s ingress links than could b e forw arded out its egress link(s), the router
has to mak e a decision whether to forw ard them in the order they arriv e, prioritize
one kind of traffic o v er another, whic h pac k ets to discard when its queues are full,
etc. Queue sc heduling algorithms, suc h as First In First Out (FIF O), W eigh ted F air
Queuing (WF Q), Lo w Latency Queuing (LLQ), etc are used to determine the next
pac k et that should b e forw arded out the egress interface.
Qualit y of Service (QoS) is the to ol that net w orking devices use to differentiate be-
t w een v arious classes of traffic in order to prioritize their forw arding as they flo w
through the device. This priorit y is usually with regards to bandwidth, delay , jit-
ter and pac k et loss. F or example, some applications, such as non-in teractiv e data
bac kup, require m uch bandwidth, with dela y and jitter remaining less critical. An-
other application, suc h as v oice, requires less bandwidth, but b etter (lo w) dela y , no
jitter and no pac k et loss to p erform optimally . Y et others, suc h as video conferenc-
ing, require m uc h bandwidth lo w delays, lo w jitter and no pac k et loss to p erform
optimally . Thus, the goal of QoS is to pro vide the differen t t yp es of traffic, differen t
asp ects of its QoS feature as is required b y eac h of them to function optimally .
2.7 Net w o rk Measurement
Measuremen t is an essen tial part of ever y engineering undertaking. F or net works
suc h as the In ternet, these measuremen ts are generally driv en b y three main goals:
so cial, commercial and tec hnical [ 42 ]. In order to effectiv ely measure the net w ork
and correctly in terpret the results, an understanding of its arc hitecture is essen tial.
31

Chapter 2 Ov erview of the In ternet
Measuremen ts pro vide ob jectiv e records and b enc hmarks ab out the net w ork’s b e-
ha vior under giv en conditions. Through measuremen t of p erformance metrics, a
net w ork’s p erformance impro v emen ts or degradation, e.g. resulting from c hanges
(willful or not) can b e quan tified and analyzed.
2.7.1 A ctive Measurement
A ctiv e measuremen ts in v olv e adding traffic that serv es as measuremen t prob es to the
net w ork. The added traffic could affect the b eha vior of the net w ork and thus distort
the results of the measuremen t. F or example, to measure the maxim um capacity of
a link, activ e prob es are con tin uously sen t through it, with increasing pack et sizes,
un til the link b ecomes saturated, at whic h p oin t the maximum v alue is recorded.
Since this could b e coun ter-pro ductiv e, the effect of the activ e measuremen t pro cess
needs to b e k ept minimal.
2.7.2 P assive Measurement
P assiv e measuremen t on the other hand, is done by observing, capturing and analyz-
ing normal net w ork traffic already generated b y other users and applications. The
measuremen t pro cess do es not need to generate its o wn traffic in order to capture the
prop erties it needs to measure. There is one p oten tial problem with this approac h
though. Since the measuremen t system dep ends on traffic that others generate,
there migh t not b e enough of the particular t yp e of traffic needed, to accurately
capture the in tended measuremen t prop ert y .
2.7.3 Hyb rid Measurement
Hybrid measuremen ts com bine elemen ts of b oth activ e and passive measuremen ts
in their function. F or example, in scenarios that in v olv e activ ely sending prob es
through a net w ork and passiv ely monitoring their progress during the measuremen t
session. Suc h allow the path of the prob es to b e trac k ed and en tities lik e inter-
mediate and end-to-end dela ys to b e recorded. This can’t b e done through activ e
measuremen t alone. It m ust ho w ev er b e noted that hybrid measuremen ts often share
the same kind of issues that activ e and passiv e measuremen ts resp ectiv ely hav e.
2.7.4 Common Metrics
P erformance metrics are used to predict the p erformance of a system under certain
conditions. The particular system b eing studied will normally dictate the type of
metrics to b e selected. T o assess the p erformance of net w orks, the follo wing metrics
are commonly used:
32

2.7 Net w ork Measurement
• Capacit y: Capacit y is a measure of the quan tity of traffic that a system can
handle. It is t ypically measured in bits p er second (bps) or pac k ets p er second
(pps).
• Throughput: Throughput measures the rate at whic h data is sen t through
the net w ork, b y coun ting the bytes that are deliv ered within a sp ecified time
windo w. This time windo w needs to b e selected intelligen tly in order to cap-
ture short-term spik es or drops as w ell. This might ho w ev er mean collecting
data at m uc h higher rates, th us requiring more system resources and capacit y .
Dep ending on the lev el of sensitivit y desired, time windo ws in 1 to 5-min utes
buc k ets are recommended. Throughput can b e measured in bits p er second,
b ytes p er second or n um b er of pac k ets p er second.
• Go o dput: Go o dput measures the amoun t of useful application-lev el data that
is transmitted p er unit time. It excludes all proto col o v erhead information,
suc h as pac ket headers and an y other data in v olv ed in the transfer pro cess,
ev en retransmitted data.
• Link Utilization: The utilization of a link is simply a ratio of the traffic cur-
ren tly b eing pushed through the link in bps to the link’s ph ysical (maxim um)
capacit y in bps, expressed as a p ercen tage.
• Dela y: Dela y is the amoun t of time tak en to transmit a pac k et from its source
to its destination. This is often referred to as end-to-end or one-w a y dela y .
• Latency: Latency is an expression of the dela y that pac kets experience while
tra v ersing the netw ork. Net w ork latency is measured b y means of Round
T rip Time (R TT) , whic h is the length of time that surpasses b et ween sending
a request from a source system to a destination system un til the time the source
system receiv es a corresp onding reply from the destination system. In other
w ords, this is the time it tak es a pac k et to go from the source to the destination
and bac k. This encompasses i) the time it tak es the pac k et to tra v el through
the ph ysical links on its path (transp ort time) ii) the time it tak es the pac ket
to go through all in termediate routers on its path (queuing and transmission
times) iii) the time it tak es the destination to pro cess the pac k et and send
bac k a reply (destination resp onse time).
• P ac k et Loss: Net w ork pac k et loss is the fraction of pac k ets that are lost
in transit within a sp ecified time in terv al, expressed as a p ercen tage of the
total traffic that w as sen t within that same time interv al. P ac ket loss giv es
an indication of the lev el of congestion in the net work or the lev el of ph ysical
impairmen t in a transmission medium e,g, cable breakage in wired links and
magnetic or electrical in terference in wireless or mobile connections.
• Jitter: Jitter is the measure of the difference in dela y that subsequen t pac k ets
exp erience while tra veling from a common source to a common destination. It
is simply the c hange in latency from pac ket to pac k et.
33

Chapter 2 Ov erview of the In ternet
2.7.5 Common Measurement and Monito ring T o ols
• PING: PING is a common prob e to ol used for activ e net w ork measuremen ts
[ 120 ]. It runs on end-hosts, as w ell as on intermediate systems suc h as switc hes
and routers and is often supplied as part of the Op erating System (OS) of the
device. The ping utility in the source system is used to generate and send
In ternet Con trol Message Proto col (ICMP) [ 163 ] e cho r e quest messages to a
target destination system. It then starts a timer when it sends off the mes-
sages. The target system simply rev erses the ICMP headers and sends back
the pac k ets to the source system as its corresp onding ICMP e cho r eply mes-
sages. The source system stops the timer the momen t it receiv es the resp onses
and is no w able to deduce the R TT b et ween itself and the target system. A
successful receipt of these resp onses indicates that the target system is con-
nected to and reac hable via the net work and that it is in a goo d enough state
that p ermits it to resp ond. This also indicates a functional net w ork with an
activ e path b et w een b oth systems.
• T raceroute: T raceroute is another ICMP-based to ol, whic h, like ping is also
commonly used for activ e net work measuremen ts. It uses the ICMP Time Ex-
c e e de d message as the basis for its measuremen t function. The traceroute to ol
generates and sends UDP pac k ets to a giv en target, starting with a minimal
Time-T o-Liv e (TTL) v alue of 1 for the first set of pac k ets and increasing this b y
1 for eac h subsequen t set of pack ets. In termediate routers that pro cess these
pac k ets reduce the TTL by one before passing them on. When they notice
that the TTL is zero, they drop the pac k ets, generate ICMP Time Exceeded
messages and send them bac k to the source system with their IP addresses
included. The elapsed time b et w een transmission of the UDP pack ets and
reception of the corresp onding ICMP Time Exceeded message are recorded.
• PR OBE: PR OBE [ 20 ] is a net work diagnostic tool, whic h, lik e the ping to ol,
can also b e used to query the status of an in terface. Ho w ev er, unlik e the
ping to ol, it do es not require bidirectional connectivit y b et w een the probing
and the prob ed in terfaces, but instead needs it b et ween the probing and a
pro xy in terface. The pro xy in terface can either b e on the s ame no de as the
prob ed in terface or on a neigh b oring no de, to which the probed interface is
directly connected (e.g. via local links in IPv6 netw orks). The Prob e to ol
uses ICMP Extended Ec ho functionalit y (which are disabled b y default) to
form ulate and send its request messages. Th us, for the Prob e to ol to b e
used on a device, the ISP (or net w ork op erator) first has to enable ICMP
Extended Ec ho functionalit y on that device and restrict access to it via p olicies.
F or securit y reasons, only configuration options enabled b y the ISP , will b e
accessible to legitimate users.
• Simple Net w ork Managemen t Proto col (SNMP): SNMP is an ubiq-
uitous net w ork managemen t to ol that pro vides lots of information ab out the
34

2.7 Net w ork Measurement
op erational status of net work managemen t elemen ts. It functions via a p olling
op eration, whereb y a Net work Managemen t Station (NMS) is used to p oll and
retriev e collected measuremen t data from differen t managed elemen ts on the
net w ork.
• Remote Net w ork Monitoring (RMON): RMON is standardized net work
monitoring sp ecification that allo ws v arious net w ork agen ts and console sys-
tems to exc hange net work monitoring data. RMON can b e set to monitor
a sp ecific set of features and p oll their data, whic h are stored in databases
kno wn as Managemen t Information Base (MIB). RMON-1 [ 210 ] provides link-
la y er statistics for Ethernet (i.e Ehternet, F ast Ethernet and Gigabit Ethernet)
in terfaces. It pro vides the abilit y to filter and capture pack et con ten ts as w ell
as the generation of alerts and alarms when thresholds are exceeded. SNMP
can then b e used to send suc h alarms to a cen tral monitoring station. RMON-2
[ 211 ] includes MIBs that extend the RMON-1 arc hitecture to include analysis
that go w a y up to the Application lay er.
• Netflo w: Netflo w [ 35 ] is a flo w-b ased net w ork and traffic monitoring and
analysis proto col dev elop ed b y Cisco Systems. A flo w is generally a unidi-
rectional sequence of IP pac k ets that p ossess the same attributes (i.e. source
address, destination address, source p ort, destination p ort, la y er 3 proto col
t yp e, T yp e of Service (T oS) and switc h/router interface). Netflo w enables the
monitoring, collection and analysis of traffic volumes and flo ws as they en ter or
lea v e in terfaces. It can also b e used to iden tify the applications that generate
the observ ed traffic and the prop ortion of bandwidth that each application is
consuming. Netflo w has four main comp onen ts, whic h enable it to monitor,
exp ort, collect and analyze data.
– Netflo w Monitor is the comp onen t that collects flow information on
device in terfaces. The monitored data is recorded and stored in cac he.
– Neflo w Exp orter aggregates data in to flo ws that are exp orted to col-
lectors as flo w records.
– Netflo w Collector is a cen tral serv er that collects and stores all flo w
records sen t b y the remote exp orters in monitored devices. UDP is used
for the data transfer.
– Netflo w Sampler is used to reduce the load on the device running
Netflo w. It do es so b y limiting the n umber of pack ets selected for analysis,
thereb y sacrificing monitoring accuracy for device p erformance.
Netflo w can also b e used for securit y analysis and accoun ting/billing. Deep er
net w ork insight is th us p ossible with Netflo w than is p ossible with SNMP .
There are similar flo w-based tec hnologies developed by other man ufacturers,
e.g. JFlow [ 149 ] from Juniper, Cflo w [ 78 ] from Lucen t an d sFlo w [ 179 ], whic h
35

Chapter 2 Ov erview of the In ternet
is join tly dev elop ed b y 3COM/HP , DEll and Netgear. Ho w ev er, none of these
is as p opular as Netflo w.
• sFlo w: Sampled Flo w (sFlo w) is a sampling technology for monitoring traffic
in data net w orks con taining switc hes and routers [ 159 ]. It provides general
purp ose sampling at la y ers 2 through 7. It com bines interface coun ters and
flo w samples in to sFlow datagrams that are sen t across the net w ork to an
sFlo w collector.
• IPFIX: The In ternet Proto col Flow Information Exp ort (IPFIX) [ 34 ] is a net-
w ork flo w standard that was created to dev elop a common, univ ersal standard
of exp ort for flo w information from routers, switc hes, firew alls, and other in-
frastructure devices. IPFIX defines ho w flow information should be formatted
and transferred from an exp orter to a collector.
36

3
Net w o rk T raffic Management
In this chapter, we fo cus on the tr affic management chal lenges that ISPs fac e on IP
b ackb one networks. W e start with an intr o duction of some of these chal lenges and
p oint out how they ar e imp acting p erformanc e. W e then pr esent some of the c ommon
solutions, as wel l as some of the most imp actful metho ds/te chniques/pr op osals that
r ese ar chers and op er ators ar e using to addr ess and curb these chal lenges/issues.
Net w ork T raffic Managemen t (NTM) (or T raffic Engineering (TE)) is a collection
of tec hniques that seek to address p erformance-related ev aluation and optimization
issues in op erational IP net w orks [ 14 , 38 ]. It encompasses a design and impro v emen t
pro cess, whic h starts with the ev aluation of technological/scien tific principles and
tec hniques, with resp ect to measuremen t, mo deling, c haracterization and con trol of
traffic and ends with the application/implemen tation of these principles and tec h-
niques on the op erational IP net w ork, with the goal of ac hieving sp ecific p erformance
ob jectiv es.
NTM has t w o ma jor ob jectiv es:
i) the timely addressing of traffic-orien ted p erformance requiremen ts.
ii) the enhancemen t of net work resources and net w ork traffic p erformances, through
economical and reliable use of a v ailable resources.
Dep ending on the sp ecific ob jectiv e and taking the dynamic nature of traffic flo ws
and traffic v olumes in to consideration, the required timescale to address related
issues could range from just a few min utes (e.g. when dealing with spikes and bursts),
up to a few y ears (e.g. when top ological adjustmen ts are needed to accommo date
gro wth-forecasts or quell p eak demands).
In general, the o v erall traffic management task includes;
• Capacit y planning with the use of traffic load forecast,
• Equipmen t configuration and managemen t,
• Net w ork usage/load monitoring,
37

Chapter 3 Net w ork T raffic Managemen t
• General traffic engineering and other approac hes that help impro v e net w ork
op erations and p erformance [ 13 ] [ 42 ] [ 54 ] [ 61 ] [ 160 ].
Core net w orks hav e differen t traffic managemen t requiremen ts and c hallenges than
user-facing access net w orks do. While div erse and easily implemen table solutions
exist for user access net w orks, impro v emen ts on core and backbone net w orks are
usually more complex and costly .
3.1 T raffic Management Challenges
As has already b een men tioned, curren t traffic gro wth rates on bac kb one netw orks
imp ose moun ting challenges on the ISP’s abilit y to con trol and manage this critical
section of its infrastructure. Their business mo dels and the net w ork arc hitecture are
b oth forced to adapt to rising and erratic demands for bandwidth. In addition, the
demand for other p opular business services (e.g. wholesale and transit, priv ate-lines,
mobile bac khaul and w a v elength switc hing) con tin ues to gro w rapidly as w ell. These
services share the same underlying optical transp ort infrastructure, up on whic h the
In ternet and IP services are built. They also rely on the a v ailable bac kb one capacit y
to op erate efficien tly . ISPs con tin ue to add bac kb one capacit y (including enough
reserv es to absorb una voidable spik es) as a means to k eep up with the high and
gro wing demand.
A ugmen ting the capacity on bac kb one net w orks is a costly undertaking, whic h un-
fortunately is also b ecoming a riskier one [ 196 ]. The is b ecause the rev en ue p oten tial
from the carried traffic is increasingly falling short of the asso ciated cost required
to augmen t the capacit y . The reasons for this are partly tec hnical, partly arc hitec-
tural and partly organizational. Ho w ev er, they are all ro oted in the w a y backbone
arc hitectures ha ve traditionally been designed, built and expanded. Gro wth and
expansion rely on linear scaling metho ds, suc h as adding comp onen ts to increase
capacit y , although the main issue they w an t to address is itself often nonlinear in
nature. Ev en tually , a critical p oin t is reac hed, where, linear scaling only leads to
decreasing returns. Better metho ds for addressing gro wth on bac kb one net w orks are
th us needed. Metho ds that concurren tly address m ultiple dimensions (e.g. the IP
and optical con trol planes), remo v e arc hitectural b oundaries and eliminate the inef-
ficiencies impacting the virtual pac k et lay er, as w ell as the ph ysical optical la y er 1 .
3.2 Co re Net w o rk Architectures
Prop osals to address the traffic c h allenge on bac kb one net w orks include c hanging
their arc hitectures, to mak e them more flexible and less costly . This leads to new
1 W e use the term physic al optic al layer to represen t the optical TDM and D WDM la y ers
38

3.2 Core Net w ork Architectures
arc hitectures that deviate a w a y from the full IP core arc hitecture. Initially , there
are predominan tly t w o alternativ e arc hitectures that result from these prop osals;
the hol low and le an core arc hitectures.
• Hollo w Core Arc hitecture: With Hollo w core arc hitectures, exp ensiv e core
bac kb one routers are replaced with a transp ort switc hing function (an optical
transp ort (OTN) switc hing lay er) that offers m uc h less total cost p er bit for a
giv en in terface sp eed.
The switc hes create a dense mesh of circuits b et w een eac h of the edge and
p eering no des. Ho wev er, the managemen t of the optical pac k et and ph ysical
optical la y ers remain separated from eac h other. The con trol-plane in tegration
is also v ery limited or non-existen t. As a result, sharing of top ology information
b et ween the virtual pac k et la y er and the ph ysical optical la y er is not p ossible.
This lea v es the routers to know only the routing topology and the optical
switc hes only the optical top ology .
• Lean Core Arc hitecture: Lean core arc hitectures are adaptations of full IP
arc hitectures comp osed of backbone routers with reduced Netw ork Pro cessing
Unit (NPU) functionalit y or memory . With reduced NPU memory , the routers
can only carry out limited routing functionalities, suc h as learning only in ter-
nal routes, whic h forces op erators to us e less memory-in tensiv e forw arding
sc hemes, suc h as MPLS instead of IP .
On the one hand, using routers with limited NPUs sinks the o v erall cost of
deplo ymen t. On the other hand, the lean core arc hitecture and its asso ciated
Lab el Switc h Rou ters (LSR) can p oten tially in tro duce problems that consume
all p oten tial savings. With this t yp e of architecture, all IP services are mo v ed
outside of the bac kb one, to the edge and Pro vider Edge (PE) routers, ev en tu-
ally con v erting the backbone to an inner-core. Suc h changes are usually com-
plex and disruptiv e to implemen t, b ecause they often in v olv e re-arc hitecting a
net w ork in activ e op eration.
Just lik e hollo w core arc hitectures, lean core arc hitectures also lac k the p ossibil-
it y to in tegrate the different la y ers, whic h con tin ue to b e managed separately .
T op ology information remains isolated within the virtual pac k et and ph ysical
optical lev els, therefore limiting the op erational efficiency .
Despite the cost-sa ving adv an tages offered b y hollo w and lean core architectures,
they still do not address the other c hallenges asso ciated with isolated pac k et and
optical la y ers. These include difficulties in monitoring, troublesho oting, pro vision-
ing, service v elo cit y , etc. Other arc hitectures are therefore needed, whic h b etter
in tegrate these isolated and often indep enden tly op erated IP and optical net w ork
la y ers (con trol planes) [ 45 ].
39

Chapter 3 Net w ork T raffic Managemen t
3.2.1 Multi-La y er Control Plane
T o address the lac k of integration betw een the la y ers, a consolidated m ultila y er
con trol plane that w orks across the pack et and optical la y ers, is created [ 195 ].
Generalized MPLS (GMPLS) is a go o d example of an arc hitecture with a m ulti-
la y er control plane. There are t w o general mo dels of GMPLS op erations; the p eering
mo del and the o v erla y mo del.
• P eering Mo del: In the p eering mo del, a single domain con taining b oth the
pac k et and optical lay ers is formed. T op ology information is shared b et w een
La y er 1 and 3 devices. La y er 3 routers th us ha v e visibilit y in to lay er 1 transp ort
paths, loads, risk groups, w a v elengths, etc. La yer 3 routers are th us able to
calculate b est paths, request for circuits that meet particular requiremen ts,
mo v e and restore failed circuits etc.
Figure 3.1: GMPLS P eering mo del (Courtesy of Cisco Systems, Inc)
On the flip side, this mo del creates 3 distinct issues;
i) The single routing domain it forms, do es not resp ect existing b oundaries.
ii) The pac k et routing and optical switching devices m ust scale to cop e with
larger routing domains, adding to memory requiremen ts and computa-
tional load across the whole net w ork.
iii) soft w are testing and upgrades m ust include b oth the pac k et and optical
la y ers, whic h slo ws do wn the certification pro cess and complicates deplo y-
men ts.
40

3.2 Core Net w ork Architectures
• Ov erla y Mo del: In the o v erla y mo del, the pac ket and optical la y ers remain
separated, but use User-Net w ork In terfaces (UNI) to in teract with eac h other.
The pac k et lay er acts as a clien t that requests for information from the optical
la y er, which acts as a serv er.
Figure 3.2: GMPLS Ov erla y mo del (Courtesy of Cisco Systems, Inc)
T op ology information is not shared b et we en the t w o la y ers, ho w ev er, the pac k et
la y er can request for circuits to b e created and to b e remo v ed using the UNI
in terface. This is the only service that the UNI in terface offers.
A main dra wbac k of this mo del is its lac k of information sharing (or only very
limited information sharing) b et ween the pac k et and the optical la y ers. It
pro vides only a limited impro vemen t in the circuit setup automation. Man y
of its other functions still need to b e done man ually , defeating the goal to ease
op erational complexities and costs.
Neither of the t w o mo dels presen ted ab o v e provides a satisfactory solution. There
is either to o m uch information sharing (with the P eering mo del) or to o little infor-
mation sharing (with the Ov erla y mo del).
3.2.2 Converged Architecture
T o sim ultaneously address current traffic c hallenges across all la y ers, without sac-
rificing one in fa v or of the other and to impro v e scalabilit y , increase flexible and
reduce cost, a new arc hitecture is prop osed. It is kno wn as the conv erged arc hitec-
ture [ 45 ].
In the con v erged architecture, all comp onen ts of the bac kb one net work are unified
in to one arc hitecture, whic h facilitates effectiv e sharing of information b et w een them.
41

Chapter 3 Net w ork T raffic Managemen t
These comp onen ts include: the pack et la y er, the optical la y er, the con trol plane, the
managemen t plane, administrativ e systems, Net w ork Managemen t (and monitoring)
Systems (NMSs), as w ell as Op erating Supp ort Systems (OSSs).
Eliminating the b oundaries b et w een these la y ers and comp onents, while enhancing
the features and roles of the con trol plane and NMSs, will aid information distribu-
tion and sharing across ev en organizational b oundaries. This creates a more efficien t
and dynamic bac kb one net w ork [ 197 ].
Although net w ork op eration and managemen t is largely economic-driv en, with cost,
regulations and tec hnology all pla ying meaningful roles, our fo cus in this thesis is
mainly tec hnological. W e ho w ev er consider the equally imp ortan t cost and regula-
tory asp ects as giv en constrain ts.
3.3 Co re Capacit y Planning
Planning the capacit y of an IP bac kb one (core) net w ork is an imp ortan t undertaking
that precedes its construction and subsequen t upgrades. V arious lev els of aggregated
traffic coming from attac hed access and p eering netw orks ough t to tra v erse the core
without running in to an y issues. F or core net w orks, b andwidth is the most imp ortan t
commo dit y . The planning pro cess should ensure that enough bandwidth is made
a v ailable across all sections of the net work. All load conditions, including traffic
fluctuations, traffic gro wths and no de/link failures, are exp ected to b e satisfied at
all times, in order to guaran tee committed SLAs.
Effectiv e planning tak es four ma jor requiremen ts into consideration:
• The core net w ork top ology
• A ccurate measuremen t of current traffic load
• F orecast of future traffic load
• Effectiv e bandwidth pro visioning metho d
Eac h of these items pla ys an imp ortan t role in ensuring that capacit y alw a ys exceeds
the demand and is not (or only minimally) affected b y failures. More detailed
explanation of these requiremen ts are giv en in the following sections.
3.3.1 Backb one Net w o rk T op ology
A common c haracteristic of bac kb one top ologies are v ery p o werful (core) routers
and v ery fast bac kb one fib er links. Data exc hange b etw een In ternet systems often
tra v el across multiple domains/bac kb ones b efore reac hing their final destinations.
42

3.3 Core Capacit y Planning
Bac kb one traffic is thus an aggregate of traffic from m ultiple sources to m ultiple
destinations.
T o measure the amoun t of traffic flowing through the bac kb one, a corresp onding
top ological represen tation of the backbone netw ork is needed. The level of details
included in the represen tation (suc h as AS-level, P oP-lev el, Net w ork-lev el or IP-lev el
details) should corresp ond with the degree of traffic aggregation and the gran ularit y
of the in tended measuremen t results.
P eering b etw een pro viders often o ccurs in m u ltiple lo cations, resulting in m ultiple
ingress/egress p oin ts, through whic h traffic could b e exc h anged. Therefore, it is
p ossible that similar flo ws through the same bac kb one netw ork could b e using dif-
feren t ingress/egress p oints. An appropriate top ology map is one that con tains suc h
necessary details as w ell.
W e presume that eac h ISP b est kno ws its o wn top ology . Detailed top ology informa-
tion constitutes confiden tial business secret that can not b e freely made a v ailable to
the public. Although high-lev el top ology information are sometimes made public,
they often do not con tain the lev el of details or accuracy corresp onding to the real
top ology . The I nternet T op olo gy Zo o [ 124 ] is an initiativ e that collects and pro cesses
suc h publicly a v ailable top ology datasets from ISPs across the w orld. Internet re-
searc hers th us rely on inference as an alternativ e metho d to acquire represen tativ e
top ology maps [ 23 , 183 , 219 ]. How ev er, imp ortan t asp ects suc h as route div ersit y
are often either lac king or incomplete. Impro v emen t is offered b y [ 146 ], using m ul-
tiple routers (instead of a single router) in AS top ologies, to enhance the accuracy
of capturing path div ersities from/through the resp ectiv e ASes.
F actors that affect top ological changes and/or traffic flo ws [ 167 ] should also b e tak en
in to consideration. F or example, un til recently , transit bac kb one net w orks of Tier1
ISPs carried the bulk of all In ternet’s in ter-domain traffic. Ho wev er, ev er since the
insurgence of CDNs, a large p ortion of this traffic no w flo ws directly b etw een them,
hosting/CDN net w orks and consumer netw orks [ 130 ]
3.3.2 T raffic Demand Measurement
T raffic matrices are essen tial for most net w ork p erformance and analysis studies. In
section 3.3.1 , we explained wh y represen tativ e bac kb one top ology maps are indis-
p ensable for accurate traffic measuremen ts. The results of suc h measuremen ts are
stored as elemen ts in a traffic demand matrix. An elemen t denotes the v olume of
traffic p er timescale that w as measured b et w een a pair of origin-destination no des.
No des in the top ology map could represen t domains, P oPs, net w orks or routers.
Measuremen ts o v er short time in terv als, i.e. from a few microseconds to a few min-
utes, are generally used to analyze p erformance issues with short timescale prop er-
ties. Lik ewise, long timescale measuremen ts, that range from min utes to y ears are
43

Chapter 3 Net w ork T raffic Managemen t
used to tac kle net w ork engineering issues of longer timescales. In practice, different
measuremen t metho ds are used, as a result of suc h timescale differences [ 42 , 49 , 144 ,
158 , 206 ].
A ccurate measuremen t of the traffic demand is quite challenging with curren t meth-
o ds. Direct measuremen t is done b y gathering and storing imp ortan t statistics e.g.
using SNMP and NetFlo w for IP net works or Link State Protocol (LSP) statistics of
Lab el Distribution Proto col (LDP) and Resource Reserv ation Proto col (RSVP) for
MPLS net w orks. It is not alw a ys p ossible to accurately measure ev ery elemen t of the
traffic demand matrix within the same time frame and under the same conditions.
Routing p olicy c hanges or other c hanges that affect flo ws and the paths they use,
could in tro duce inaccuracies or errors that falsify the measuremen t. Estimates are
often used to supplemen t where measuremen ts are either incomplete, practically not
p ossible or are p ossible but lead to inaccurate results. A n um b er of these estimation
metho ds are ev aluated in [ 81 ]. The b est estimation approac hes capture netw ork and
flo w dynamics to help eliminate sources of error and impro ve their lev els of accuracy
[ 53 , 98 , 182 ].
3.3.3 T raffic Demand F o recast
F orecasting is a k ey readiness factor when planning to accommo date future gro wths.
T raffic demand forecasts are predictions of future loads in an ticipation of such
gro wths. They pro vide the basis for prior p erformance ev aluation and analysis.
Go o d forecasts dep end on tec hniques that use a com bination of historical and cur-
ren t datasets to predict the future loads. Ho wev er, ev en the b est forecasts are still
error-prone b y considerable margins.
3.3.4 Bandwidth Provisioning
Pro visioning ensures that enough bandwidth is allo cated to accommo date demands,
while sim ultaneously guaran teeing that all p erformance attributes defined in SLAs
with customers are main tained. F actors that influence the quan tit y of the bandwidth
to b e pro visioned, include; exp ected/unexp ected traffic gro wths, bac kups in case of
failures/main tenances, changing usage patterns and other activities that affect the
v olume and path of traffic as it flo ws through the net work.
An efficien t pro visioning approach is essen tial to a v oid w astage and minimize cost.
Instead, most ISPs use simple rules of thum b approac hes to o v er-provision bac kb one
links, b y as muc h as 10-folds in extreme cases. Ov er-pro visioning ensures that there
is enough capacit y in the net w ork to meet demands, esp ecially during p eak times
and under failure conditions.
44

3.4 Net w ork and T raffic Engineering Approac hes
The 40% or 50%-rule, requires links to b e upgraded when their a v erage utilization
exceeds 50% [ 201 ]. Another approac h recommends normal op eration up to an a v er-
age link utilization of t ypically 35%, then to upgrade when this increases to a v erages
b et ween 40 and 60% of the link capacit y . More recen t approac hes recommend a v er-
age utilizations as high as 80 - 90% b efore upgrading, for traffic made up of a mix
of man y flo ws [ 59 ].
Sev eral prop osals exist that request the c hange and impro v emen t of the curren t
status quo, including implemen ting new arc hitectures at the core [ 44 ], as presented
in Section 3.2 .
3.4 Net w o rk and T raffic Engineering App roaches
New er and b etter approac hes are needed to resolv e traffic issues on backbone net-
w orks. Curren t approac hes fo cus on solving a single dimension of what is actually
a m ultidimensional problem.
The goal of net w ork engineering is to alter the netw ork to matc h the traffic flo wing
through it. On the other hand, the ob jectiv e of traffic engineering is to alter the traf-
fic flo wing through the net work, so that it matc hes the top ology . Either approac h or
a com bination of b oth could b e used to obtain an optimized net w ork infrastructure,
as will b e sho wn later.
T raffic engineering pla ys the distinctiv e role of con trolling and optimizing the routing
function. The goal is to influence the traffic that flows through the net w ork, so that it
is forw arded in suitable w a ys that satisfy one or more c hosen p erformance ob jectiv es.
Suc h ob jectives include; reduction of a verage pac k et dela ys, a v oidance of net w ork
congestions, traffic load balancing across m ultiple paths and traffic rerouting around
failed links and devices.
3.4.1 Soft w a re and Ha rdw a re Upgrades
The In ternet is driv en b y sp eed; sp eed of the transp ort media, sp eed of the for-
w arding hardw ares (routers and switc hes), sp eed of the soft wares that driv e these
hardw ares, sp eed of the applications, sp eed of the serv ers/systems that host these
applications, etc. Rising sp eeds to a large exten t, together with other imp ortant
factors, accoun t for the observ ed go o d general p erformance and impro v ed user ex-
p erience on the In ternet.
Net w ork Service Providers are taking adv an tage of adv ances in soft w are and hard-
w are tec hnologies to upgrade their infrastructures. They are replacing legacy devices
with state-of-the-art devices p ossessing features that offer sup erior technical, p er-
formance, managemen t and cost b enefits.
45

Chapter 3 Net w ork T raffic Managemen t
The man ufacturers of these devices w ork together with researchers to realize and im-
plemen t suc h key features, whic h then get integrated in to subsequen t soft w are/hardware
releases and mo dels. Man ufacturers strive to bo ost p erformance and confidence in
their pro ducts b y addressing kno wn (soft w are) ca v eats and releasing frequent bug-
fixes and up dates that customers are exp ected to implemen t as they b ecome a v ail-
able. They w ork together with their customers to gather feedbac ks on suc h fixes, as
w ell as on new features that the devices offer.
Emerging trends that cause shifts in the w a y things are done, often also warran t new
kinds of services and (sometimes also) sp ecial devices to run or supp ort the services.
Soft w are-Defined Netw orking, Virtualization and Cloud-hosted services are trends
curren tly impacting the net w orking industry in general and ISP’s in particular. An
appropriate infrastructure needs to b e put in place to supp ort the service landscap e
that the curren t trend has called in to existence. This simply implies hardw are and/or
soft w are upgrades are needed to get the infrastructure ready . Inspite of all these,
the one factor that seems to accompan y all ma jor trends on the In ternet, is the
constan tly increasing traffic v olumes and flows that ISPs ha v e to deal with.
With regards to net w orking devices, adv ancement in c hip tec hnology has led to the
man ufacture of devices that are more p ow erful and faster in b oth pro cessing and
transmission sp eeds [ 148 , 194 ]. Line sp eeds of up to 100Gbps on a single channel
are curren tly attainable with these new devices [ 150 , 151 , 193 ].
Although the scale of suc h upgrades offer v arious tec hnical and business b enefits,
on the flip side, they sometimes also require high initial inv estmen ts that in turn,
in v olve longer and tedious justification processes b efore the budget is gran ted.
3.4.2 A dditional No des and Links
The ph ysical top ologies of most backbone netw orks remain unc hanged o v er relativ ely
long p erio ds of time. A ddition of new no des and/or links causes c hanges to the
top ology that need careful planning and analysis prior to installation. A more
common practice with ISPs is to upgrade existing no des and links without necessarily
c hanging the top ology . Rapid traffic gro wths ha v e ho w ev er comp elled Pro viders
to upgrade bac kb one links and/or no des (routers/switc hes) at increasingly shorter
in terv als. Still and all, a p oin t then comes when the top ology also needs to gro w,
esp ecially when the load gro ws to lev els unsustainable using curren t upgrade means.
A dding new no des/links b ecomes an inescapable necessit y .
Dep ending on the traffic dynamics, the ISP can decide to only add new links b et we en
existing no des to feed high demands. The new link can therefore b e added as:
• a parallel link to existing link(s), thereb y b o osting the bandwidth b et ween the
t w o no des, or,
46

3.4 Net w ork and T raffic Engineering Approac hes
• a completely new link b et w een no des where none existed b efore. This adds a
new path to the top ology , resulting in a new top ology .
In b oth cases, the addition affects IGP routing metrics, whic h further influence
the paths that flo ws tak e. Prior ev aluation is th us an imp ortant and una v oidable
requisite.
A lot more needs to b e tak en in consideration when adding new no des. No des alw ays
need to b e attac hed to other no des. Therefore, new links also need to b e planned
wherev er new no des are b eing added to a top ology .
A dding new no des/links is usually a very costly in v estmen t for the ISP . On the
flip side, falling price p er b yte turns to reduce (or ev en negate) the ISP’s return
on suc h in vestmen t. ISPs therefore follow the simple and less expensive approac h
of preferring the shortest p ossible ph ysical distances when considering where to
add/mo v e links/no des in the top ology .
3.4.3 Change of T raffic Matrix
T raffic matrices pro vide clues on wh y the traffic distribution in a net w ork is the
w a y it is and the effects that c hanges w ould ha v e. TMs are sub ject to c hange with
c hanges in usage patterns and trends. This means factors affecting to da y’s TM
migh t not necessarily b e the same factors affecting tomorro w’s. T o ac hiev e sp ecific
goals, the TM could b e influenced or c hanged by c hanging the routing p olicies that
affect them.
A t ypical scenario in volv es traffic demands with flo ws b et w een domains, i.e. flo ws
that transit via dedicated ingress links through to a set of egress links on an ISP’s
bac kb one. The demand mo del allo ws prediction of ho w c hanging the in ternal routing
impacts the distribution of load on the ISP’s bac kb one links [ 53 ].
Another approac h uses V aliant Load Balancing (VLB) to supp ort all p ossible traffic
matrices [ 220 ]. VLB is based on a fullmesh top ology supp orting equal load balancing
across 2-hop paths b et w een an y ingress and egress no de of the bac kb one net w ork.
In the first stage, traffic arriving at eac h no de is divided in to equal parts and sen t to
eac h of the bac kb one no des, irresp ectiv e of the final destination. In the second part,
eac h in termediate no de sends its part of the traffic to the ultimate destination.
3.4.4 Flo w Rerouting
A further w a y to influence the traffic that flo ws across bac kb one net w orks is to alter
their path, b y means of flo w rerouting. Flo w rerouting o ccurs either as a resp onse to
c hanges in net w ork conditions, c hanges in securit y and routing p olicies, preparation
for net w ork main tenance or as a means of ac hieving a desired Qualit y of Service
(QoS) goal.
47

Chapter 3 Net w ork T raffic Managemen t
A big adv an tage of flo w rerouting is its applicabilit y/suitabilit y in resolving issues
of smaller timescales. F or example, it can b e used to a v oid b ottlenec ks, reduce
congestions, balance traffic loads and b ypass failures in a matter of min utes.
3.5 Net w o rk Optimization
A bulk of the problems faced b y ISPs could b e seen as optimization pr oblems in-
v olving decision making, for example, on ho w and when to upgrade their net w ork
to increase its capacit y [ 83 ].
Generally , optimization is either minimizing or maximizing a giv en function relativ e
to some set of a v ailable c hoices in a giv en situation [ 173 ]. Researc hers and net w ork
op erators use optimization theory to study net work behaviors, analyze their effects
and optimize the use of a v ailable resources. The optimization problem is defined as
a computational problem in whic h the ob jectiv e is to find the b est of all p ossible
solutions [ 59 ].
The parameters to decide up on are called de cision variables . Only in rare cases are
these v ariables p ermitted to tak e on an y v alue from −∞ to + ∞ . Their v alues are
often instead limited b y variable b ounds . The decision to b e made usually dep ends
on m ultiple input p ar ameters that are either giv en or first need to b e determined,
b efore the decision is made. The obje ctive function is a function of the parameters
and v ariables. It has to b e minimized or maximized via the optimization pro cess. In
doing this, certain restrictions ha v e to b e defined and main tained. These restrictions
are called c onstr aints .
With regards to traffic engineering in a net w ork, optimization seeks the b est w a y to
route traffic through the net w ork in order to attain stated ob jectiv es, while honoring
defined constrain ts.
3.5.1 Routing Limitations
IP routing is purely destination-based forw arding, with the c hosen path b eing the
one that offers the b est (smallest) total metric (cost). Ho w ev er, the criteria used to
determine the metric is lac king in some practical asp ects. T aking the link-state in tra-
domain routing proto col, OSPF, as an example, although calculation of its metric
emplo ys link bandwidth, it completely ignores the link load, whic h is of a b etter
significance. On the other hand, link load is a v ery dynamic prop ert y . Including
it in the metric’s calculation w ould in tro duce a v olatile prop ert y that could affect
net w ork stability and p erformance. This is b ecause an y c hange in the load would
trigger routers to recalculate new metrics, up date their neigh b ors and then w ait for
con v ergence. This could even tually result in the selection of differen t paths for the
48

3.5 Net w ork Optimization
same subnet, as w ell as cause route flapping and instabilit y , whic h are detrimen tal
to the net w ork’s general p erformance.
Ev en tually , since only the b est paths are c hosen, this could lead to ov er-utilization
(congestion) on some links while others remain minimally used or are not used at all.
Optimization (as w e shall sho w in Chapter 6) therefore presents a better alternative
that could b e used to redistribute traffic flo ws within a net w ork and minimize the
maxim um load on individual links.
3.5.2 Net w o rk Graphs
Graphs are mathematical structures that represen t pairwise relationships b etw een
ob jects. Represen ting a problem as a graph offers a differen t p oin t of view on
the problem that can mak e it m uch easier to solv e. They find application in man y
business and engineering domains, including netw ork optimization. Graphs are made
up of v ertices (no des) and edges (links) that connect the v ertices.
F o rmal Definitions
F ormally , a graph G is defined as G = (V, E), where V is the set of all v ertices
and E is the set of all edges in the graph. Oth er attributes of graphs are defined as
follo ws:
Ro ot v ertex/no de: The ro ot v ertex is the ancestor of all other v ertices in a
graph. It therefore has no paren t of its o wn and is usually the access p oint in to a
graph.
Leaf v ertex/no de: A leaf v ertex is one without an y successor. It can ha v e man y
incoming edges, but no outgoing one.
Simple graph: A simple graph has no self-lo ops and has only a single edge b et ween
an y t wo v ertices.
Undirected graph: An undirected graph is one in whic h all the edges are bi-
directional.
Directed graph: A directed graph is one in whic h all the edges p oin t in a single
direction only (uni-directional).
W eigh ted graph: A w eigh ted graph has each of its edges assigned an asso ciated
w eigh t or cost. This w eigh t is giv en b y the function w : E → R .
49

Chapter 3 Net w ork T raffic Managemen t
Structural Prop erties
Some fundamen tal structural prop erties of the graph G=(V, E) are describ ed as
follo ws:
Connectivit y: A graph is c onne cte d when it is p ossible to reac h a v ertex from
an y other v ertex in the graph. It is str ongly c onne cte d when there is a direct
connection b et ween an y t w o v ertices in the graph (fullmesh top ology). A graph
that is disc onne cte d can b e split up in to a n um b er of connected c omp onents .
A djacency: A finite graph can b e represented using an adjac ency matrix or an
adjac ency list . An adjacency matrix A is a 2-dimen tional | V | x | V | binary matrix,
where | V | is the n um b er of v ertices in the graph. An elemen t A ij has the v alue 1
if there is an edge from v ertex i to v ertex j , else A ij = 0 . F or a w eigh ted graph,
the v alue of A ij is that of the corresp onding w eigh t or cost.
An adjacency list is a more v ertex-cen tric w a y of represen ting the same information
ab out the graph.
Degree: In an undirected graph, the de gr e e of a v ertex deg ( v ) is the n um b er of
edges inciden t on the v ertex. In a graph with n v ertices, deg ( v ) ≤ n − 1 ∀ v ∈ V .
In a directed graph, the inde gr e e deg − ( v ) of a ver tex is the n um b er of edges coming
in to the v ertex and the outde gr e e deg + ( v ) is the n um b er of edges leaving the v ertex.
W alks: A w alk of length k is a sequence of alternating v ertices and edges, suc h
as v 0 , e 1 , v 2 , e 2 , ..., e k , v k . Eac h edge e i is given b y e i = { v i − 1 , v i } . W alks can hav e
rep eated edges. A w alk is close d if its starting and ending v ertex are the same, i.e.
if v 0 = v k . Else, it is considered op en . A trail is a w alk with no rep eated edges.
P aths: A path is an op en trail with no rep eated v ertices. A shortest path is the
minim um path connecting an y tw o v ertices.
T op ological distance: The top ological distance d ij b et w een v ertex i and v ertex
j is the n um b er of edges in the shortest path connecting b oth of them.
A distanc e matrix D is a | V | x | V | matrix with D = ( d ij ) , where d ij is the top ological
distance b et ween v ertex i and v ertex j .
Cycles: A cycle is a closed trail, where no other v ertices are rep eated apart from
the one where it starts/ends.
T rees: A tree is an undirected graph in whic h an y t w o v ertices are connected by
one and only one path. In a graph with n v ertices, a tree is an acyclic graph with
n-1 edges. In a graph, eac h v ertex ma y ha v e one or more paren t. In a tree, eac h
v ertex has only one paren t, except for the ro ot v ertex that has no paren t.
Some of the ab o ve defined properties will b e applied in our study and analysis of
the P2P o v erlay in Chapter 4, as w ell as in form ulating and solving the optimization
problem in Chapter 6.
50

3.5 Net w ork Optimization
3.5.3 Mo deling and Solving Optimization Problems
T o solv e optimization problems, one starts with fi rst iden tifying the exact problem,
stating the v ariables, the constrain ts, the ob jectiv e function and the parameters.
Next, the ob jectiv e function and constrain ts are form ulated as mathematical mo dels.
Thereafter, they are solv ed using standard approac hes, suc h as those men tioned
b elo w. T o detect if there are p oten tial issues with the mo del, small problems are
often first solv ed and their solutions carefully analyzed. Only after the correctness of
the solution has b een confirmed and the confidence enhanced, should more complex
problems b e attempted. Since input parameters can sometimes b e uncertain or
incorrect, sensitivit y analysis is usually done to find out ho w sensitiv e the solution
is to c hanges of input parameters.
The t w o most p opular mathematical programming approac hes used to mo del and
solv e optimization problems are Line ar Pr o gr amming (LP) [ 147 ] and Inte ger Pr o-
gr amming (IP) [ 26 ], resp ectiv ely [ 6 ]. While decision v ariables in LP problems are
allo w ed to b e con tin uous (or fractional) in v alue, in IP they tak e on discrete in teger
v alues. F urther v arian ts of IP are pro vided b elow.
In linear programming, the mathematical expressions for the ob jective function and
the constrain ts are all linear. Linear programming is the most widely used metho d
of constrained optimization. One seeks to find a set of v alues for the con tin uous
v ariables ( x 1 , x 2 , ..., x n ) that minimizes or maximizes a linear ob jective function
z , while satisfying a set of linear constrain ts (in the form of sim ultaneous linear
equations and/or inequalities). LP problem definition and mo deling can inv olv e
plen t y of v ariables and equations, as m uch as mill ions of v ariables and h undreds of
thousands of constrain ts [ 28 ]. In general, an LP problem can b e mathematically
expressed as follo ws [ 26 ]:
Maximize z = X
j
c j x j (3.1)
sub ject to X
j
a ij x j ≤ b i ( i = 1 , 2 , ..., m ) (3.2)
x j ≥ 0 ( j = 1 , 2 , ..., n ) (3.3)
An in teger programming problem is an LP problem in whic h at least one of the
v ariables is limited to in teger v alues only . In a pur e IP problem, al l the decision
v ariables must b e integers. When the v ariables are limited to v alues of either 0 or 1,
the t yp e of IP in v olv ed is called a Binary In teger Programming (BIP).
A Mixe d-Inte ger Pr o gr amming (MIP) optimization problem is one in whic h some of
the decision v ariables are real-v alued (i.e. can b e fractional) and others are in teger-
v alued (i.e. can tak e on only integer v alues) [ 26 , 213 ]. The mo del is therefore
51

Chapter 3 Net w ork T raffic Managemen t
referred to as "mixed". When the ob jectiv e function and constrain ts are all linear,
the mo del is called a Mixed In teger Linear Programming (MILP) mo del. When
nonlinear v ariables are in v olved, the mo del is referred to as Mixed-In teger Nonlinear
Programming (MINLP) and is usually more harder to solv e. In most cases, MIP is
commonly used to mean MILP . In general, the mathematical form ulation of an MIP
optimization problem is of the form:
Maximize z = X
j
c j x j + X
k
d k y k (3.4)
sub ject to X
j
a ij x j + X
k
g ik y k ≤ b i ( i = 1 , 2 , ..., m ) (3.5)
x j ≥ 0 ( j = 1 , 2 , ..., n ) (3.6)
y k = 0 , 1 , 2 , ... ( k = 1 , 2 , ..., p ) (3.7)
It should b e noted at this p oin t that the input parameters ( c j , d k , a ij , g ik , b i ) ma y b e
p ositiv e, negativ e or zero. The ab o ve set of expressions can also b e stated in matrix
notation as follo ws:
Maximize z = c T x + d T y (3.8)
sub ject to Ax + Gy ≤ b (3.9)
x ≥ 0 (3.10)
y ≥ 0 in teger (3.11)
where m = n um b er of constrain ts
n = n um b er of con tin uous v ariables
p = n um b er of in teger v ariables
c T = ( c j ) is a ro w v ector of n elements
d T = ( d k ) is a ro w v ector of p elements
A = ( a ij ) is an m x n matrix
G = ( g ik ) is an m x p matrix
b = ( b j ) is a column v ector of m constan ts (or righ t-hand-side column)
x = ( x j ) is a column v ector of n con tin uous v ariables
y = ( y k ) is a column v ector of p in teger v ariables
When n = 0 , there will no longer b e any con tin uous v ariables x . The MIP therefore
b ecomes (reduces to) a pure in teger program. Also, when p = 0 , no in teger-restricted
v ariables y will exist and the MIP reduces to a linear program. An LP can also b e
ac hiev ed when the integer requiremen ts in a giv en MIP are relaxed (or ignored).
The LP that stems from this is called the LP r elaxation of the giv en IP . Unlik e LPs
52

3.5 Net w ork Optimization
con taining only x v ariables, this LP relaxation con tains b oth x and y v ariables and
treats y as a v ector of con tinuous v ariables.
53

4
Managing P2P T raffic via Collab o ration
This chapter sp e cific al ly de als with P2P systems and the tr affic management chal-
lenges attribute d to their disruptive natur e and high b andwidth c onsumption affinity.
W e start with an intr o duction of P2P systems, then elab or ate on their kinds and
uses. W e then p oint out some of their str engths and we aknesses, including pr op osals
and c ontributions fr om the r ese ar ch c ommunity on how to de al with some of these
we aknesses and issues. L astly, we pr esent our pr op ose d solution, the Or acle servic e,
showing how it effe ctively functions as an enabler for ISP and P2P c ol lab or ation. W e
pr esent multiple simulation studies and analyses, which show the numer ous b enefits
of the servic e to b oth the ISP and the P2P user.
In Chapter 1, w e sa w that In ternet bac kb one traffic has gro wn to gigan tic prop ortions
o v er the last decade [ 108 ]. W e also sa w that its steady increase p oses p ersisten t
managemen t c hallenges to ISPs.
Since net w ork op erators alw ays ha v e to plan appropriately w ell ahead of time, re-
sources for gro wths are usually made a v ailable via short, mid or longterm planning.
Ho w ever, ev en ts and trends that suddenly cause significan t spik es and increases in
traffic v olumes and flo ws, also call for quick er shorter-term managemen t resp onses.
Often, suc h c hanges cannot b e ignored or p ostp oned, esp ecially when they directly
or indirectly impact other services. P eer-to-P eer file sharing is an example of suc h
a phenomenon that w arran ted b oth short and long term resp onses. It accoun ted
for more than 50% of the In ternet’s bac kb one traffic [ 114 ] [ 136 ] at the p eak of its
p opularit y . Although this is no longer the case, P2P traffic is still resp onsible for
a significan t fraction of In ternet traffic in some regions. F or example, as recen tly
as 2016, BitT orren t 1 [ 37 ] accoun ted for the highest fraction of uplink traffic for
fixed access connections in North America. Its share sto o d at 18.37%, compared to
only 13.13% and 10.33% resp ectiv ely for Y outub e and Netflix, in the same category
[ 207 ].
1 BitT oren t is currently the most popular P2P application proto col used for file-sharing.
55

Chapter 4 Managing P2P T raffic via Collab oration
4.1 P eer-to-P eer (P2P) Systems
P eer-to-p eer, as it is kno wn to da y , basically denotes a t yp e of distributed comput-
ing system, in whic h mem b ers (p eers) come together to share suc h resources as,
con ten t, storage or CPU pro cessing p o wer. In a P2P system, eac h p e er represen ts
a participating end-host that functions b oth as a clien t and as a server to other
p eers. P eers connect with other p eers to form neigh b or-relationships and establish a
logical o v erla y net w ork at the application la y er. Although comm unication b et w een
neigh b oring p eers app ears to o ccur directly at the o v erla y la y er, in realit y , their
corresp onding underla y no des migh t b e man y hops apart. Each peer in the o v erla y
net w ork represents a forw arding no de, similar to routers in the ph ysical underlay
net w ork. Ho w ev er, the o v erla y is used mainly for p eer disco v ery and for index-
ing, while all data exc hanges b et ween the peers o ccur via TCP using the ph ysical
underla y net w ork.
4.1.1 Unstructured P2P Systems
Unstructured P2P o v erlays are those that are established arbitrarily , i.e. without
an y defined global structure. P eers rely on their adjacen t neigh b ors for pac k et
deliv ery to other p eers. Message propagation o ccurs via flo o ding and random walks
[ 21 ]. Although the lac k of a mandatory global structure eases the establishmen t of
the unstructured o v erlay net w ork, the same can also lead to a sub optimal o verla y
top ology that is still robust under high c h urn. There is th us ro om for optimization,
e.g. via lo calization, in v arious segments of the o v erla y net w ork. The most prominen t
example of an unstructured P2P system is Gn utella [ 123 ]. Our P2P studies in this
thesis are based on Gnutel la version 0.6 implemen tation.
4.1.2 Structured P2P Systems
Ov erla ys in structured P2P systems are established according to a predefined crite-
ria. The most common t yp e uses a Distribute d Hash T able (DHT) to assign con ten t
o wnership to particular p eers. In this case, hash functions are used to map p eers and
to reference shared con ten t, on to a common iden tity space [ 119 ]. This iden tit y space
consists of (k ey , v alue) pairs that are stored in a database, allo wing participating
p eers to retriev e an y v alue b y reference of its asso ciated k ey .
4.1.3 P erfo rmance Challenges
The v ery nature of P2P systems that brings adv an tages suc h as robustness, scalabil-
it y and high con tent a v ailabilit y , also accoun ts for some of its ma jor w eaknesses, suc h
56

4.1 P eer-to-P eer (P2P) Systems
as high c h urn, high signaling traffic, free-riding, etc. A n um b er of these c hallenges
are further elab orated b elow.
• Bo otstrapping: New p eers that need to join a P2P o v erla y first need a list
of already existing and preferably online p eers to select the ones to establish
connections with. A base list can b e supplied as part of the clien t application,
whic h is up d ated (via do wnload) during first-time use or a preferred list is
gotten from a third-part y and then man ually added to the configuration file.
Irresp ectiv e of how the list of p eers is obtained, p otential neigh b ors are selected
from it, either at random or based on some preferred criteria, suc h as pro ximit y
[ 22 ], role, e.g. as Sup erno de [ 135 ] or influence, as in the Swarm I ntel ligenc e
A ppr o ach [ 87 ]. The n umber of neighbor-relationships that a new p eer can
establish is finite. It is dep enden t on man y factors, suc h as the size of the list,
ho w curren t it is and the num b er of its p eers that are also online at the time
of connection.
• Unpredictabilit y: The p erformance of the P2P net w ork is largely influenced
b y the nature of its o verla y arc hitecture, i.e the n umber of participating p eers,
their con tributed resources and ho w they in terconnect with eac h other. A com-
mon issue with P2P net w orks is the dynamic nature of the p eer mem b ership
and neigh b or relationships. The system is based on v oluntary participation,
meaning, p eers join and lea v e the o v erla y net w ork whenev er they w an t. How-
ev er, this v ariabilit y also in tro duces unpredictabilit y in the system, causing
its reliabilit y , scalabilit y and ev en p erformance to b e impacted, when large
n um b ers of p eers are simultaneously affected.
• Efficiency: Earlier unstructured P2P systems, suc h Gnutella, use flo o ding to
forw ard queries from a requesting p eer to other p eers in the net w ork. A p eer
form ulates a searc h query and sends it to all its directly attached neigh b ors.
These neigh b ors c hec k in their databases and resp ond accordingly , in case there
are hits. They then forw ard the same query string to all of their o wn neigh b ors.
These next lev el of neigh b ors also searc h lo cally and resp ond accordingly b efore
forw arding the searc h string y et again to their o wn set of neigh b ors, and so
on. Suc h flo o ding feeds on a v ailable bandwidth. As a result, a single search
can pro duce a m ulti-fold increase in o v erhead traffic.
The w a y the o v erla y top ology of a P2P net w ork is established also affects the
cost and efficiency of comm unication in the P2P system. Ov erla ys constructed
via random connections b et ween the peers often p ossess little or no correlation
with the ph ysical routing underla y [ 5 , 91 , 133 , 134 ].
• F ree-riding: The main reason wh y P2P systems w ere created (and b ecame
so p opular), w as to enable participating users to freely share their resources.
Ho w ever, it w as quickly disco v ered that most participating p eers are selfish
[ 180 ]. They consume the system’s shared resources but con tribute little or
nothing to it [ 80 ]. Such peers are called fr e e-riders [ 1 ]. F ree-riding is a common
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Chapter 4 Managing P2P T raffic via Collab oration
phenomenon in file-sharing P2P systems, where studies show that only a small
p ercen tage of the p eers contribute a large p ercen tage of the shared files [ 94 ,
176 ].
4.1.4 Imp rovements
There ha v e b een sev eral impro v emen t prop osals/implemen tations to the original
P2P applications and systems. Starting with b o otstrapping, [ 125 ] analyzes existing
metho ds used for b o otstrapping, then mak es a n um b er of prop osals for their im-
pro v ement. F or unstructured P2P systems, [ 73 ] presen ts an approac h, whic h do es
not rely on host or w eb cac hes, but on DNS-based profiling.
The structure of the Gn utella o verla y w as c hanged from a single flat la yer to one
with t w o hierarchic al la y ers. T w o categories of p eers were in tro duced, ultr ap e ers and
le af-no des . Ultrap eers b elong to the top hierarc hical lev el. They are c haracterized
b y long session lengths and high con ten t a v ailabilit y . They are also often highly
connected with man y other p eers. Leaf-no des b elong to the lo w er level. They
join the o v erlay b y connecting to ultrap eers and often ha v e m uc h shorter session
lengths.
The metho d that is used to propagate queries across the Gn u tella o v erla y has also
b een impro ved. It no w uses targeted flo o ding instead of con trolled flo o ding [ 189 ]. In
addition, information from connected neigh b ors and p ong (resp onse) messages are
cac hed and subsequen tly used to resp ond faster to similar queries without needing
to flo o d the net w ork again.
Man y prop osals for change/impro v emen t, including incen tives affecting user behav-
ior ha v e b een made [ 52 , 139 , 218 ]. Some of these ha v e already b een partly integrated
in to P2P applications and systems to discourage free-riding and impro v e fairness and
con ten t a v ailabilit y . Despite these initiativ es, free-riding and con ten t a v ailabilit y still
remain ma jor issues in P2P systems un til date.
4.2 The Oracle Service
No one kno ws a net work lik e the ISP that o wns and op erates it. Some applica-
tions, suc h as P2P , require this kno wledge in order to op erate more efficiently . F or
example, kno wledge of the net w ork could help p eers built more efficien t o verla ys
that align prop erly (b etter) with the ISP routing underla y . This will further im-
pro v e ov erla y routing, as w ell as search and do wnload p erformances. Ho w ev er, no
ISP readily hands out information ab out its net w ork to third parties, b ecause of
business and securit y reasons. Applications therefore attempt to infer net w ork con-
ditions themselv es, whic h often pro duces less than accurate results and m uch traffic
o v erhead.
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4.2 The Oracle Service
Not withstanding, the ISP’s in terest in main taining an efficien t and high-p erforming
net w ork, calls for its in volv emen t in resolving suc h issues. After all, impro v ed net-
w ork efficiency and p erformance b enefit b oth the P2P systems and the ISP . So, can
P2P systems and ISPs co op erate to ac hiev e a win-win solution for b oth parties? W e
sa y "Y es!".
T aking all of the ab o v e into consideration, w e th us prop ose a solution that:
• encourages and enhances P2P-ISP collab oration
• resolv es/minimizes the mismatc h issue
• b o ost P2P and net w ork p erformance
• is simple and cost-effectiv e to implemen t/op erate
• reduces in ter-AS traffic asso ciated with P2P to manageable lev els
W e prop ose an ISP-offered free service, whic h w e call the Or acle . It enables p eers
to mak e informed and b etter c hoices ab out p oten tial neighbors to connect with
when b o otstrapping and p oten tial sources to do wnload con ten t from after a searc h
resulting in m ultiple hits.
Figure 4.1: Collab oration using the Oracle service
The principle b ehind the Oracle service is quite simple. As an ISP-hosted service,
it has access to detailed information ab out their net w ork and the connection details
of their end-users, suc h as, each user’s respective lo cation, bandwidth and link de-
la ys. With suc h accurate kno wledge of the net w ork and its dynamic conditions, the
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Chapter 4 Managing P2P T raffic via Collab oration
Oracle service is b est p ositioned to assess p oten tial p eers and rank them according
to differen t preferences or criteria, suc h as,
i) mem b er of lo cal or remote AS
ii) distance to edge of the AS
iii) AS hop coun t based on BGP metric
F or p eers b elonging to the same AS as the Oracle, it can further rank them according
to:
i) bandwidth (connection sp eed)
ii) link dela y
iii) pro ximit y
iv) curren t (op erational) status, e.g. a v ailable bandwidth and dela y
When p eers use the Oracle service, e.g. when b o otstrapping to join the o verla y or to
do wnload from p otential peers after a search quer y , they follo w the 3-step approac h
sho wn in Figure 4.1 , as follo ws:
• Step 1 : Send the unsorted list of p oten tial p eers to the Oracle serv er, option-
ally indicating the desired ranking criteria.
• Step 2 : The Oracle serv er sorts the list accordingly and sends bac k to the
p eer.
• Step 3 : The p eer uses the rank ed list to connect to or do wnload from neigh b ors
that are preferably rank ed at the top of its sorted list.
Since p eers are not comp elled to use this free service, the system is designed in
suc h a w a y that, it offers great and pro v able incen tives that attracts ev en the most
sk eptical users to at least test it. P eers that use the Oracle service b enefit in the
follo wing w a ys:
• Only a little c hange is needed b y p eers to use the service
• P eers often need to mak e informed decisions that require knowledge of the
underla y net work. They no longer need to measure or infer these themselv es,
but can simply rely on the Oracle service for this
• Impro v ed user exp erience. A correlated Ov erla y-Underla y and proximit y b e-
t w een neigh b oring p eers, help a v oid congestions at in ter-AS exc hange p oin ts,
thereb y b o osting throughput and reducing latency for the p eers.
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4.3 The SSFNet Sim ulator
By sending an unsorted list to the ISP service and getting bac k a sorted one, no
further information is released ab out the p eers, apart from that whic h the ISP is
already in p ossession of. This, in effect, handles an y priv acy concerns the users
migh t ha v e.
On the other hand, b y offering the free Oracle service as an incentiv e to p eers, the
ISP b enefits in the follo wing w a ys:
• Without the service, the disruptiv e nature of P2P traffic will con tinue to be a
bane. The Oracle service effects a more correlated o v erla y-underla y b y influ-
encing ho w p eers connect with each other, whic h in turn influences neigh b or
relationships and ho w traffic flo ws b et ween them.
• Analysis of P2P traffic flo ws sho w that they unnecessarily cross AS b ound-
aries man y times o v er, despite the presence of the same con ten t within their
same AS. ISPs prefer to minimize cross-b oundary traffic, in order to preven t
increased transit c harges. By offering the Oracle service whic h addresses their
cause, the ISP is able to regain con trol of a substan tial fraction of the cross-
b oundary traffic.
• Being able to manage a large fraction of disruptiv e traffic, recreates ro om for
fair usage alongside other applications.
4.3 The SSFNet Simulato r
The name SSFNet w as formed b y combining SSF and Net , each of whic h repre-
sen ts a ma jor comp onen t of the SSFNet mo deling and sim ulation softw are. SSFNet
is used to mo del and sim ulate complex large-scale IP net w orks and offers pac k et-lev el
gran ularit y .
4.3.1 Scalable Soft w a re F ramew o rk (SSF)
Scalable Soft w are F ramew ork (SSF) is a standard-based mo deling language. It is
used to create ob ject-orien ted mo dels of v arious elemen ts used in a sim ulation. The
SSF en vironmen t has 5 fundamen tal classes; En tities, Pro cesses, Ev en ts and In-
Channels and Out-Channels [ 18 ].
• En tities are ob jects with the abilit y to p ossess pro cesses and c hannels, whic h
enable them connect with eac h other. An entit y can send and receiv e data
within the sim ulation en vironment and can be monitored to tak e accoun t of
its pro cesses and data transactions.
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Chapter 4 Managing P2P T raffic via Collab oration
• Pro cesses con trol the request and generation of information b y en tities. Pro-
cesses that b elong to differen t en tities can run sim ultaneously , as a result of
an implemen ted fairness p olicy that preven ts a single pro cess from running
more than once during the same sim ulation timeframe. A pro cess can b e in
one of the follo wing states; ready to run, running, susp ended or w aiting for a
resource. There is a sc heduling pro cedure within SSF that sc hedules pro cesses
that are ready to run. Susp ended pro cesses that are w aiting for a sp ecific
sim ulation timeframe ha ve priorit y o v er those w aiting for resources.
• Ev en ts con trol the sim ulation run. They simulate data traffic and con trol
ho w en tities handle the same. Ev en ts can b e sa ved and be released d uring
pro cessing, making monitoring p ossible. They can also use aliases to create
p oin ters to other ev ents.
• inChannels are lik e in terfaces of an en tit y , through whic h data is receiv ed.
The In-c hannel of one en tity connects to the Out-c hannel of another en tit y ,
from whic h it receiv es the even ts that it needs to pro cess.
• outChannels are in terfaces of an en tit y through whic h data is sen t. Data
that is pro duced b y pro cessed ev en ts and need to b e sen t to other en tities, get
sen t via the Out-c hannel.
4.3.2 SSFNet Overview
SSFNet is a collection of Ja v a SSF-based comp onents used to model and simulate
In ternet proto cols and netw orks at and ab o v e the IP pac k et lev el of detail [ 40 ]. Its
main classes, with whic h basically all In ternet mo dels can b e created, are organized
under t w o ma jor pac kages; the SSF.OS pac kage and the SSF.Net pac kage.
The SSF.OS pac kage is used to mo del hosts and op eration system comp onents, suc h
as proto cols, while the SSF.Net pac kage is used to mo del net work connectivit y and
to create no de and link configurations. Both pac kages help hide the details of the
discrete ev en t simulator, causing it to implemen t the proto cols just lik e it is done in
real OSes.
The SSF.OS P ackage
The main classes in the SSF.OS pac kage are:
• Proto colGraph: defines the proto col used in a host
• Proto colSession: defines the metho ds of comm unication that proto cols use
• Proto colMessage: defines the pack et used in the Proto colSession to carry
sim ulated data
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4.3 The SSFNet Sim ulator
This pac kage also con tains Internet protocol mo dels built on top of the base SSF.OS
mo del, e.g. SSF.OS.IP , SSF.OS.TCP , SSF.OS.UDP , SSF.OS.OSPF, etc, that are
used to mo del IP , TCP , UDP and OSPF proto cols resp ectiv ely .
The SSF.Net P ackage
The main classes in the SSF.Net pac kage include:
• Net: mo dels a net work. It loads the mo del from a DML file and controls all
instances of the mo del.
• Host and Router: mo dels a net w ork host as a deriv ativ e of SSF.OS.Proto colGraph,
with added net w orking attributes. Mo dels a router as a sp ecial host with m ul-
tiple NICs.
• NIC: mo dels net work in terfaces for hosts and routers.
• Link: mo dels link-la y er connectivit y b et w een attac hed host and/or router
in terfaces.
Domain Mo deling Language (DML)
Domain Mo deling Language (DML) is a high-level model description lan guage that
uses standardized syn tax. The syn tax sp ecifies a list of attributes (key-v alue pairs)
that can b e stored in ASCI I readable/writable files. The DML pac kage included
in SSFNet aids it in describing and configuring mo dels. All deriv ativ e framew orks
created on top of the SSF API are able to use the DML pac kage to configure mo dels.
The format used for configuration is; k ey follo wed b y the v alue in brac k ets, whic h
indicate the start and end of the v alue, i.e.
key [value]
In case of m ultiple k ey-v alue pairs, spaces or carriage returns could b e used to
separate them, as sho wn b elo w.
key1 [value1] key2 [value2]
key3 [value3]
SSFNet mo dels can individually configure and instan tiate themselv es b y querying
DML-formated files from the net w ork.
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Chapter 4 Managing P2P T raffic via Collab oration
4.4 Collab o ration within a P2P Simulation Environment
In this section, we look into some fundamen tal asp ects of the ISP/P2P collab oration
offered b y the Oracle service. W e start b y implemen ting the Gnutella P2P protocol
in the SSFNet sim ulation en vironment, then design and mo del a represen tativ e In-
ternet w ork consisting of m ultiple domains and mix of Tier1, Tier2 and Tier3 ISPs.
W e distribute end-hosts within eac h AS according to their category (see T able 4.1 ).
Eac h host uses the Gn utella P2P proto col to join the o v erlay and b ecome an activ e
p eer.
W e use this setup to assess the Oracle’s abilit y to function as an enabler of ISP/P2P
collab oration, rectifier of the P2P o v erla y/ISP underla y mismatc h, enhancer of ISP
p erformance and impro v er of the general end-user exp erience.
4.4.1 System Design
W e use the SSFNet soft w are to mo del and sim ulate the collab oration en vironmen t
con taining ISP underla y and P2P ov erla y infrastructures.
T able 4.1: Net work Properties
In total, there are 25 ASes, 50 routers and 1000 P2P hosts in the net w ork, distributed
as sho wn in T able 4.1 . T aking memory constrain ts in to consideration, we limit the
size of the net w ork by using only 2 routers per AS. One router is dedicated to
in ter-AS connections, while the other serv es as the in tra-AS router, used to attac h
end-hosts to the net w ork. Link dela ys b et w een Tier-1 and Tier-2 ASes and b et w een
Tier-2 and Tier-3 ASes are set at 2 msecs and 10 msecs resp ectiv ely .
P eers that join the o verla y top ology , tak e on one of t w o p ossible roles, le af-no de
or ultr ap e er . Leaf-no des establish connections to a minim um of 2 and a maxim um
of 4 ultrap eers. Ultrap eers ha v e at least 10 connections to other p eers. They stop
accepting connection requests when the coun t reac hes 45. The n um b er of files a p eer
can share is uniformly distributed b et ween 0 and 100. P eers sta y online for at least
one second and at most 1500 seconds. Once they go offline, they only rejoin after
a p erio d b et ween 1 - 300 seconds. This adds the effect of ch urn to our exp erimen t.
P eers functioning as leaf-no des can transcend to ultrap eers only after ha v en b een
online for at lest 600 seconds.
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4.4 Collab oration within a P2P Sim ulation En vironmen t
A p eer uses its lo cally sa v ed hostcache to establish connections with other peers
(p oten tial neighbors). The hostcac he is simply a list of p eers that migh t ha ve been
seen on the net w ork in the past, but with no guaran tee that they are online at
the time of connection establishmen t [ 82 ]. The Oracle service can help a p eer sort
its hostcac he, according to proximit y preference, whic h biases the establishmen t of
neigh b or relationships to fa v or p eers in close pro ximit y to eac h other. The Oracle
uses the follo wing algorithm to sort the list it receiv es from a p eer:
i) First, iden tify p eers in the same AS as the requesting p eer and place them at
the top of the sorted list
ii) Then, use AS-distance to sort the rest of the p eers not in the same AS as the
requesting p eer
W e use the ab o v e setup to run three separate exp erimen ts based on the follo wing
cac hed file-sizes:
• 1000 and not using the Oracle service
• 100 and uisng the Oracle service
• 1000 and using the Oracle service
All three exp erimen ts hav e the same n um b er of queries and similar resp onse success-
rates.
W e run m ultiple test simulations, for v ariable lengths of time and notice that very
little c hanges o ccur b ey ond 5000 seconds. So, we settle with 5000 seconds for eac h
sim ulation run.
The results and analyses of the sim ulations are presen ted in the following sub-
sections.
4.4.2 Graph Structural Prop erties of the Overla y
T op ology Visualization
W e in vestigate the top ological impact of using the Oracle service and use visualiza-
tion to compare the P2P o v erla y structure when no Oracle service is used with the
case when the Oracle service is used.
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Chapter 4 Managing P2P T raffic via Collab oration
Figure 4.2: T op ological Visualization of the P2P Ov erla y
After the initialization phase (whic h is ab out 500 seconds), we let the sim ulation run
again for a reasonable time and then start sampling the o v erlay topology to capture
all p eers that are online at the time of sampling. Links are drawn b et w een p eers
that share neigh b oring relationships. W e then use the visualization library from
yW orks [ 77 ] to con vert these relationships in to the structural hierarc hical format
sho wn in Figure 4.2 . The o v erla y using the Oracle service, Figure 4.2 b p ortra ys a
structural resem blance to its underla y . Most links are formed b et w een p eers in the
same AS, visualized as areas of thic kly p opulated dots and lines. Only a relativ ely
small n um b er of links connect to p eers in external ASes. Suc h a correlated structure
is completely absen t in the o verla y not using the Oracle service, as sho wn in Figure
4.2 a.
Graph Diameter
The diameter of a graph (or net w ork) is the greatest distance (coun ted in hops)
b et ween an y t w o no des of the graph (or net w ork). Since w e use the same underla y
top ology to test the 3 cases, the AS diameter remains the same (4 hops) in all 3
cases. W e therefore compare the diameters of the more dynamic o v erla y instead. W e
observ e that when the Oracle uses a cac he size of 100, its diameter ranges b et w een
6 and 8 hops, compared to only 5 to 7 hops, when not using the Oracle. The range
increases to 7 and 12 hops, with an an a v erage of 9.2 hops, when the Oracle uses a
list-size of 1000.
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4.4 Collab oration within a P2P Sim ulation En vironmen t
Graph Connectivit y
Without a cen tral managemen t system, it is p ossible for the Gn utella o verla y to
exist as sev eral disjoin t o v erla ys [ 176 ]. W e therefore c hec k if using the Oracle service
to bias neigh b or selection could pro v oke this effect. W e sample the netw ork after
ev ery 500 seconds and c heck if an y of the samples con tain split comp onen ts. None
of the samples in all 3 cases con tains o v erla y disjoin ts or split comp onen ts, leading
us to conclude that using the Oracle service do es not negativ ely impact o v erla y
connectivit y (or cause disjoin ts).
No de Degree
The no de degree of a p eer is simply the n um b er of links it has to adjacen tly connected
p eers. Although no de degree is an imp ortan t top ological prop ert y of an y net w ork,
in P2P systems it has a unique significance b ecause of a n o de’s triple role, as clien t,
serv er and router resp ectively . The higher the no de degree, the b etter connected
the net w ork is. W e th us use it as a metric to in vestigate the Oracle’s impacts on the
P2P Ov erla y’s structural/top ological prop erties.
Figure 4.3: A v erage no de degree of Ultrap eers
F or a Gn utella ov erla y that is comp osed of ultrap eers and leaf-no des, ultrap eers
can, b y design, main tain a m uc h higher num b er of connections with other p eers
than leaf-no des. Ho w ev er, in our analysis, we notice a similar no de degree pattern
among b oth t yp es of no des. W e therefore rep ort only on that of the more significan t
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Chapter 4 Managing P2P T raffic via Collab oration
ultrap eers. Figure 4.3 sho ws the a v erage no de degree of ultrap eers in eac h of the
3 cases. Despite a generally decreasing trend with time, for all 3 cases, the no de
degree decreases the most for the Oracle case with a list-size of 1000. Its largest
difference of 4.54 units o ccurs at 3500 second in to the sim ulation.
W e also notice that the Oracle case with a list-size of 1000 started off ha ving a
sligh tly higher a v erage no de degree than that with a list-size of 100. Ho w ev er, with
time, the latter sho ws increasingly b etter v alues than the former. Still, the a v erage
no de degree remains generally high enough to not negativ ely impact the ov erla y
top ology .
Intra-AS Connections
W e next lo ok at the prop ortion of direct neigh b or connections that ultrap eers build
with other p eers from within the same AS and compare it to their total n um b er of
connections.
Figure 4.4: P ercen tage of Ultrap eer connections within the same AS
Figure 4.4 sho ws the p ercen tage of in tra-AS to total connections formed by ultra-
p eers. The prop ortion stagnates with time, for the un biased case. Mean while, it
increases for b oth cases of Oracle with 100 and 1000 file sizes resp ectiv ely . The high-
est p ercen tage difference of 74.95% to the un biased case is recorded at 3500 seconds
of sim ulation time b y the Oracle with a file size of 1000. It also sho ws significantly
b etter p ercen tage v alues than those for the Oracle case with a file size of 100.
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4.4 Collab oration within a P2P Sim ulation En vironmen t
4.4.3 User Exp erience
Queries, Resp onses and T raffic Reduction
The T raffic in P2P o verla y net w orks are of t w o ma jor categories, signaling and data
transfer. The signaling traffic consist mainly of connection negotiations and query
searc hes/resp onses. In unstructured P2P o v erla y netw orks, the signaling traffic con-
stitutes a considerable fraction of the total traffic.
T able 4.2: Num b er of queries and resp on ses in P2P Ov erla y
Gn utella queries are forw arded with a default Time T o Liv e (TTL) v alue of 7 from a
source to all its directly connected neigh b ors. The TTL v alue is reduced b y 1, eac h
time the query gets forw arded b y a successive neigh b oring p eer. F orw arding stops
when the TTL reac hes 0. Consequen tly , b ecause a query is propagated b y means
of flo o ding, a single query gets forw arded m ultiple times around the net w ork, when
searc hing for con tent.
W e tak e account of the resulting query-related traffic and the n um b er of resp onses
that get routed bac k to the original source, for each quer y hit. T able 4.2 con tains
the recorded n um b er of queries and resp onses. It sho ws that the highest n um b er
of queries are recorded at the same lev el of the propagation, i.e. when TTL=4, for
the un biased as w ell as for the biased case. The highest n umber of resp onses are
also recorded at the same lev el, at TTL=2, for b oth cases. These confirm that the
Oracle do es not hinder queries from b eing propagated man y hops a w a y from the
source nor from getting resp onses from p eers that are m uc h further a w a y .
Despite the ab o ve similarities, w e notice a drastic reduction in the query traffic
from 12.2 million to 5.6 million pac k ets, when the Oracle service is used. W e as
w ell notice that using the Oracle service causes a slight reduction in the n um b er of
resp onses, from 268.3 thousands to 242.6 thousands. In terestingly , for only a 9.6%
reduction in the n um b er of resp onses, more than half the amoun t of the hea vier query
traffic sw arm (53.87%) is a voided. W e consider this an acceptable trade-off. Suc h
large reduction in traffic helps to free up crucially needed bandwidth and impro ve
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Chapter 4 Managing P2P T raffic via Collab oration
net w ork efficiency . Net w ork efficiency b enefits b oth the ISP and end-users. P artic-
ularly though, the traffic reduction without impacting application functionalit y and
p erformance, is an added b enefit to the ISP b y the Oracle service.
Overla y P ath Length
As can b e seen in Figure 4.5 , the a v erage path length b et w een o v erla y p eers remains
virtually unc hanged (or c hanges only slightly) with time, for the un biased and 100
list-size Oracle cases. Concurren tly , a clear increase with time is recorded for the
1000 list-size Oracle case.
Figure 4.5: A v erage ov erla y path length
In spite of this sligh t increase in mean o verla y path length, since more in tra-AS
connections and exc hanges are no w o ccurring within the same AS, when using the
Oracle with a list-size of 1000, its final impacts are neutralized and sho w no effect
on the p erformance.
A verage Underla y AS Distance of Overla y P eers
The AS distance simply refers to the n um b er of domains (or ASes) separating an y
t w o p eers. This is easily determined b y mapping eac h o v erla y p eer to its underla y
AS and then coun ting the n umber AS hops b et w een them.
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4.5 Effects of T op ology and User Beha vior on Lo calit y
Figure 4.6: A v erage underlay AS distance of Ov erla y p eers
Con trary to the trend w e observed for the a v erage o v erla y path lengths in the pre-
vious sub-section, Figure 4.6 sho ws a reduction in the a v erage underla y AS distance
with time, for the 1000 list-size biased case. The maxim um reduction o ccurs at 5000
seconds, from a v alue of 1.94 to a v alue of only 0.25.
The v ery lo w av erage AS distance for the 1000 list-size biased case is simply an
indication and confirmation of increased lo calit y b ecause of the Oracle service, as
already seen in Section 4.4.2 . F or an ISP , this means retaining m uc h more traffic
within its o wn AS, whic h is a ma jor cost sa ving ob jectiv e.
4.5 Effects of T op ology and User Behavio r on Lo calit y
T o impro ve on the w ork done in the previous section, w e design our net works to
include differen t top ologies and more realistic distributions for user b eha vior. W e
also include v arious user distributions, including their access bandwidths as a further
criteria for the Oracle service to use in sorting the list of p oten tial candidates.
W e run t wo main sets of experiments. One set to analyze top ological effects and
the other set to analyze user b eha vioral effects. Eac h exp eriment is ran for t w o
distinct cases; one un biased (U) case, where the Oracle service is not used at all
and one biased (B) case, where the Oracle service is first used during b o otstrapping
and later for con ten t do wnloads. With these exp erimen ts, we seek to examine ho w
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Chapter 4 Managing P2P T raffic via Collab oration
the Oracle’s lo calit y service is affected by differen t top ologies and differen t user
b eha vioral patterns, including adv erse c h urn and con ten t rarit y .
4.5.1 P erfo rmance Metrics
T o ev aluate the exp erience of b oth the ISP and P2P user, we consider the follo wing
metrics:
• Num b er of resp onses generated p er query
• Ov erla y hop count of the responses
• Underla y AS distance of the resp onses
• Do wnload resp onse time
• Prop ortion of exc hanged con ten t retained within an AS
• Quan tit y of reduced ov erla y traffic
All files ha v e a standard size of 512KB, the same as the piece size used in most
p opular P2P systems. P eers connect with eac h other using TCP and use HTTP
to exc hange data. F or eac h unsorted list the Oracle receiv es from p eers using the
service, it sorts it as follo ws:
i) First, iden tify p eers in the same AS as the requesting p eer, sort them b y their
bandwidth and place them at the top of the sorted list.
i) Then, use AS-distance to sort the rest of the p eers, i.e. p eers not in the same AS
as the requesting p eer.
W e use the more realistic W eibull distribution to mo del online session lengths and
con ten t av ailabilit y . Queries are propagated via flo o ding. The results in each of
the exp erimen ts are based on 10.000 successful query requests that result in 10.000
successful do wnloads.
4.6 Evaluating T op ological Diversit y
T o study ho w P2P lo calit y is affected by differen t ISP underla y top ologies and the
distribution of p eers within them, we design 5 differen t AS top ologies, comprising 2
national and 3 w orld top ologies. Eac h top ology contains 700 P2P hosts, distributed
as sho wn b elo w in T able 4.3 .
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4.6 Ev aluating T op ological Div ersit y
4.6.1 Designing the T op ologies
The 5 AS top ologies include:
• German y: W e retriev ed a copy of German y ISP top ology map from [ 113 ] and
extracted the 12 biggest ISPs from it, including their in ter-AS connections.
Using the broadband user information in [ 50 ], w e distribute the 700 P2P hosts
among the 12 ISPs, according to their fraction of broadband customers.
• USA: W e mo del one regional provider per city for eac h of the 25 ma jor cities
and connect them using information obtained from [ 132 , 183 ]. Each AS (cit y)
gets a fraction of the 700 P2P hosts corresp onding to its share of the p opula-
tion.
• W orld1, W orld2, W orld3: W e mo del 3 different W orld top ologies, eac h
with a single Tier-1 AS, 5 Tier-2 ASes and 10 Tier-3 ASes, for a total of 16
ASes p er top ology . In terconnections b et w een the ASes are designed according
to routing information con tained in [ 146 ].
T able 4.3: AS and P eer distributions in the 3 W orld top ologies
The 700 p eers are distributed based on results from [ 131 , 146 ]. T able 4.3
summarizes the differen t w ays ASes and peers are distributed in the W orld
top ologies.
The ab o ve designs giv e us the p ossibilit y to study ho w differen t top ologies and p eer
distributions affect the o v erlay/underla y p erformances.
W e th us mo del these top ologies within the SSFNet environmen t, taking the mem-
ory limitations and difficulties/constrain ts in volv ed with sim ulating suc h large and
complex net w orks within such an en vironmen t, in to consideration [ 63 ].
Eac h AS has t wo routers with separate functions. One p eering (in ter-AS) router
for connections with other ASes and one user-access (in tra-AS) router for lo cal
connections with p eers. P eers are connected in a star top ology with this router.
The p eer connection sp eeds reflect the normal DSL/cab el mo dem sp eeds at the
time. W e use sp eeds ranging b etw een 1 and 16 Mbps for this. Tier-1 and Tier-2
ASes con tain larger prop ortions of higher sp eed subscrib ers [ 50 , 146 , 176 ], so w e
assign sp eeds of 10 - 16 Mbps to 80% p eers in Tier-1 AS and sp eeds of 1 - 4 Mbps
to 60% of p eers in Tier-3 ASes. W e also assign link dela ys b etw een 4 - 6 msecs to
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Chapter 4 Managing P2P T raffic via Collab oration
connections b et ween Tier-1 and Tier-2 ASes and link dela ys b et w een 18 - 20 msecs
to connections b et w een Tier-2 and Tier-3 ASes [ 132 , 221 ].
4.6.2 Mo deling User Behavio r
Studies sho w similar user b ehavioral patterns across structured and unstructured
P2P systems [ 86 , 89 , 90 ], despite con tin uous transitions. Ho w ev er, there are some
differences b et ween file sharing and video streaming P2P systems [ 92 , 216 ]. Ch urn
is one of the most w ell-studied and analyzed user b ehavioral c haracter [ 190 , 212 ].
W e use differen t distributions to simulate observ ed b eha vior, as w ell as abstract
and w orse case scenarios. W e employ sensitivity analysis to explore and determine
parameters that b est fit the distributions represen ting observ ed b ehaviors, i.e. within
the limitations of accuracy p ossible in our sim ulation en vironmen t.
Sha red Content
Con ten t replication helps improv e do wnload sp eeds and general p erformance in P2P
net w orks. How ev er, a significan t num b er of users are selfish and share nothing at all
(free-riders). This greatly impacts the ov erall a v ailabilit y of con ten t. F r e e-riding is
confirmed b y sev eral P2P measuremen t and analysis studies [ 1 , 94 , 128 , 168 , 176 ].
In conclusion, they stipulate that the num b er of files shared b y p eers appro ximates a
hea vy-tail distribution. W e th us use differen t distributions to mo del shared con ten t
in the o v erla y net w ork.
The n um b er of files that eac h p eer shares is plotted against the p eer’s ID as sho wn
in Figure 4.7 , using the follo wing distributions:
• Uniform distribution with parameters, min=0 and max=100, to represen t
the comparison baseline (Figure 4.7 a).
• P areto distribution with parameters, k=100 and alpha= 10, as one form
of a long-tail distribution with ma jorit y p eers sharing relativ ely mo derate to
small n um b er of files (Figure 4.7 b).
• W eibull distribution with parameters, scale=4.2 and shap e=0.5, to rep-
resen t another form of long-tail distribution with few p eers sharing a large
n um b er of files, while a go o d n umber of p eers are free-riding and sharing zero
files (Figure 4.7 c).
• P oisson Distribution with a mean of 50, representing the h yp othetical case
that a constan t n umber of files are shared at an y given time (Figure 4.7 d).
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4.6 Ev aluating T op ological Div ersit y
Figure 4.7: P2P con ten t distributions
In real P2P net w orks, the t yp es of con ten t that are shared, often dep end on their
p opularit y . Less p opular con ten ts are often difficult to find. Most users stop shar-
ing less p opular and outdated con tents to create/retain space for more recen t and
p opular ones.
Session Length
T w o imp ortan t c haracteristics of p eers in o v erla y net w orks, are the randomness of
their participation and the length of time they sta y online. The dynamics of this
participation is what is generally referred to as churn . Ch urn affects session length,
whic h can also b e influenced/limited by the ISP , e.g. through enforcemen t of 24
hours session timeouts [ 137 ].
Extensiv e studies ha ve been done on ch urn and online session lengths [ 86 , 190 ,
203 ], with conclusions that the latter fits a hea vy tail distribution with v arying
parameters. A go o d understanding of c h urn is necessary to effectively design and
mo del P2P systems. T o this end, w e again use four represen tativ e distributions
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Chapter 4 Managing P2P T raffic via Collab oration
to mo del the concurren t length of time that p eers sta y online without quiting or
b ouncing (i.e. session length).
Figure 4.8: Session length distributions
Session length also affects con ten t a v ailabilit y . Con ten ts can only b e successfully
shared, if the p eers that are sharing (up loading/seeding) sta y online long enough to
allo w others do wnload and seed as well.
Although do wnloads can also b e completed after m ultiple sessions, it often happ ens
that the original seeder of a particular con ten t, starts sharing, but go es offline and
do es not return for a long p erio d, or pulls bac k the con ten t for go o d, b efore other
p eers ha ve the opportunity to complete do wnloading it. Suc h incomplete do wmloads
con tribute to the fraction of shared con tent classified as junk.
Queries
There are t w o ma jor t yp es of queries, i.e. c onstant phr ases that search for sp e cific
typ es of c ontent , e.g. mp4, mp3, eb o oks, and volatile phr ases that searc h for sp e cific
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4.6 Ev aluating T op ological Div ersit y
c ontents , e.g. names/titles of authors/artists/albums/b o oks. T o enable us study
the effect of P2P lo calit y on searc hes, we model our queries to represent 45% of eac h
t yp e, mimic king their p opularit y distributions and loads, as rep orted in [ 74 , 121 ].
W e then mo del the remaining 10% to matc h no av ailable con ten t. In mo deling the
queries, we ensure that they are modeled in a wa y , whic h ensures that 20% of all
queries result in at least 1 or at most 2 hits.
4.6.3 Simulation Results and Analyses
The results of the first set of exp erimen ts in v olving differen t top ologies (and their
impacts) are presen ted in the sub-sections that follo w.
Structural Prop erties of the Overla y T op ology
Since the Oracle service is first used at b o otstrapping to effect the construction of
a more lo calized o v erla y , w e start with in v estigating the structural prop erties of the
Oracle-biased o v erlays and compare them with those of the un biased o v erla ys.
Un biased o verla ys are c haracterized b y the follo wing graph structural prop erties:
• remain connected (no disin tegration in to subgraphs) despite ch urn
• p ossess small graph diameters
• lo w a v erage path lengths
• lo w a verage node degrees
Figure 4.9: A v erage no de degree of o verla y p eers
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Chapter 4 Managing P2P T raffic via Collab oration
In comparison with Oracle-biased o v erlays, w e observ e that these to o remain con-
nected in all but a few sampled instances. Ho w ev er, these instances are only tem-
p oral, since resulting subgraphs get join t again a few seconds later.
W e also observ e a s ligh t c hange (decrease) in the a v erage no de degree of Gn utella
ultrap eers in the biased o verla y . This can b e seen in Figure 4.9 . It sho ws compar-
ativ ely small differences, of less than 3 units to those of ultrap eers in the un biased
o v erlay . USA and German y are extreme cases p ortra ying the biggest differences.
Figure 4.10: Mean Ov erla y Path Length
Figure 4.10 sho ws the a v erage o v erla y path length of the un biased and biased top olo-
gies. W e notice that although it sligh tly increases in all 5 biased top ologies, the
difference remains w ell b elow 2 magnitude p oin ts. In fact, in the extreme case of
German y top ology , where the difference is highest, it is only sligh tly ab o v e 1 mag-
nitude p oin t.
Queries/Resp onses Analysis
The effect of lo calit y on the n um b er of resp onses is sho wn in Figure 4.11 . It sho ws an
increase in the n um b er of resp onses for the biased case, across all 5 top ologies. With
increased lo calit y via use of the Oracle service, more queries get sen t to pro ximal
neigh b ors within the same AS, whic h also means m uc h more resp onses are coming
from lo cal neigh b ors within the same AS.
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4.6 Ev aluating T op ological Div ersit y
Figure 4.11: Num b er of Resp onses p er Query
Since queries are propagated via flo o ding, this also means less signaling traffic is
b eing flo o ded b ey ond an AS’s b oundaries. It even tually reduces the v olume of in ter-
AS traffic and increases the prop ortion of in tra-AS traffic.
Content Do wnloads
Understandably , users join a con ten t (file) sharing P2P o v erla y to share (do wnload
and upload) con ten t. F or most users though, sharing simply means always down-
lo ading and never uplo ading . Their sole purp ose is downlo ad at al l c osts and uplo ad
at no c ost . This is eviden t in the high lev els of selfishness and free-riding observ ed
in P2P net w orks. With do wnloads ha ving suc h high significance, w e use downlo ad
r esp onse time as the appropriate metric to quan tify the end user’s exp erience.
W e see in the b o x plot of Figure 4.12 that the a verage time tak en to do wnload a
512KB file reduces across all 5 top ologies, by 1 - 3 seconds in fa v or of the Oracle
biased case. This is equiv alen t to a reduction of 16 - 34% in do wnload times.
Do wnload sp eeds generally also dep end on the size of a candidate p eer’s last mile
bandwidth. Therefore, a p eer in a differen t AS migh t b e a b etter candid ate to
do wnload from than one in the same AS, if its upstream bandwidth is higher.
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Chapter 4 Managing P2P T raffic via Collab oration
Figure 4.12: Do wnload resp onse times
Intra-AS Exchanges
W e sa w in S ection 4.4 that using the Oracle service causes more p eers within the
same AS to form more lo calized connections with eac h other. As a result, the
prop ortion of in tra-AS exc hanges also increases. W e rep eat the same pro cedure on
the 5 top ologies b eing in v estigated in this section.
Figure 4.13: P ercen tage of resp onses from within the same AS
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4.6 Ev aluating T op ological Div ersit y
F or top ologies using the Oracle service, the b o x plots in Figure 4.13 sho w mark ed
increases in the a v erage prop ortion of resp onses coming from p eers that are within
the same AS domain as the p eer that sen t the original query . The increase is
consisten t across all 5 top ologies.
Figure 4.14: P ercen tage of conten t exc hanges b et ween P eers in the same AS
In tune with the increased prop ortion of resp onses observ ed coming from p eers
within the same AS as the p eer that sen t the query , w e also observ e a corresp onding
increased prop ortion of con ten t exc hanges within the AS, when the Oracle service is
used. Figure 4.14 shows that for eac h top ology , the p ercen tage of intra-AS exc hanges
is m uc h higher for the biased case than for the un biased case.
Inter-AS T raffic Reduction
With more resp onses to sen t queries and con ten t exc hanges b et w een p eers, no w
o ccurring within an AS domain, as a result of the Oracle lo calit y service, w e pro ceed
to analyze the c hange in the amoun t of signaling traffic that is exc hanged b et ween
ASes.
Figure 4.15 sho ws the a ve rage n um b er of pac k ets that are exc hanged p er query
b et ween ASes in eac h of the 5 top ologies. In comparing the un biased case, Figure
4.15 a, where the Oracle service is not used, against the biased case, Figure 4.15 b,
where the Oracle service is used, w e notice a h uge traffic reduction across all biased
top ologies using the Oracle service. W orld1 and W orld3 ha v e close to 6-fold and
10-fold traffic reductions, resp ectiv ely .
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Chapter 4 Managing P2P T raffic via Collab oration
This is a further confirmation of the Oracle service’s traffic managemen t abilities
and an added b enefit to the ISP . Eliminating or at least, containing a goo d p ortion
of suc h traffic sw arms within an ISP’s o wn AS, relieves bandwidth consumption on
p eering and transit links. Th us, a further ISP costs-sa ving asp ect of the Oracle
solution.
Figure 4.15: T raffic reduction across domains
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4.7 Ev aluating Changes in User Beha vior
4.7 Evaluating Changes in User Behavio r
Using a mix of national and w orld top ologies, w e’v e sho wn that b oth the ISP and the
user do b enefit from increased lo cality via the Oracle service. The b enefit remains
consisten t across differen t top ologies. In this section, w e’ll no w in v estigate if the
same holds true for differen t (extreme) user b eha vioral patterns.
W e therefore extend the user b eha vioral patterns for session length and shar e d c on-
tent b y including Uniform, P areto and P oisson distributions to the W eibull distri-
bution already used in the previous section. The total of 16 different c om binations,
resulting from the 4 shared con ten t and 4 session length distributions, offer us the
p ossibilit y to also study such extreme conditions, as adv erse c h urn in the presence
of sparsely a v ailable con tent.
Since our emphasis in this section is more on user b eha vior than on top ology , to
minimize the effects of top ology , w e select the one that has the most ev enly dis-
tributed p eers in all its ASes. W e th us select the W orld3 top ology to run the 16
exp erimen ts on.
W e then analyze the results based on the metrics outlined in Section 4.5.1 .
4.7.1 A verage No de Degree and P ath Length of Overla y P eers
Figure 4.16: A verage node degree and path length of o verla y p eers in W orld3
T op ology
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Chapter 4 Managing P2P T raffic via Collab oration
The a v erage no de degree in the un biased case is only sligh tly higher than those of
the other distributions, as can b e seen in Figure 4.16 a. The only exception is with
the P oisson session length, whic h has a difference of b et w een 4 and 8 a v erage no de
degrees to that of the un biased case. It also has the least av erage no de degree in
com bination with eac h of the 4 shared conten t distributions.
Figure 4.16 b sho ws that the a v erage o v erla y path length of the biased o v erla ys (those
based on the giv en distributions) practically fall within the same range as that of
the un biased o verla y . The only noticeable exception in the biased case o ccurs when
the session length is P oisson and the file distribution is W eibull. This is when the
highest mean o v erlay path length of appro ximately 7 is recorded.
4.7.2 Queries/Resp onses Analyses
W e analyze the resp onses that other p eers generate and send bac k to the p eer that
sen t the original query request.
Figure 4.17: A verage o v erla y hop coun t and underla y AS distance of resp onses in
W orld3 T op ology
Although Figure 4.17 a sho ws that the a v erage o v erla y hop coun t of these resp onses
are sligh tly higher for most of the biased com binations than it is in the un biased
o v erlay , Figure 4.17 b sho ws the con trary , with regards to a verage AS distance of
the underla y . This simply confirms increased lo calit y as a result of using the Oracle
service.
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4.8 Bey ond the Oracle Service
4.7.3 Intra-AS Content Exchanges and Do wnload Times
W e observ e muc h higher p ercen tages of in tra-AS conten t exc hanges across all 16
com binations of the Oracle’s user b ehavioral patterns than in the un biased case that
do es not use the Oracle service. Figure 4.18 a sho ws a difference of at least 48%
b et ween the least performing Oracle pattern (Poisson session length and P oisson file
distribution) and the un biased case.
Figure 4.18: In tra-AS Con tent Exc hanges and do wnload times in W orld3 T op ology
Corresp ondingly , Figure 4.18 b also sho ws that the a verage do wnload times of all
Oracle-based distributions are m uc h b etter (lo wer) than that of the un biased case.
The least p erforming Oracle case, with the least difference (highest a v erage do wnload
time) compared to the un biased case, o ccurs with Poisson session length and P oisson
file distribution.
4.8 Bey ond the Oracle Service
Op erating an efficien t multi-purpose netw ork, suc h as the In ternet, requires careful
planning and in telligen t allo cation of often limited resources. ISPs face moun ting
c hallenges in k eeping up with the growing proportions of traffic volumes and flo ws,
o v er whic h they ha v e only limited or no con trol. So far, c hallenges caused b y P2P
can effectiv ely b e addressed via collab oration enabled and promoted b y the Oracle
service.
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Chapter 4 Managing P2P T raffic via Collab oration
In terms of complying with securit y , priv acy and protection regulations, suc h as the
General Data Protection Regulation (GDPR) [ 209 ] of the EU, the Oracle service can
ensure that no information receiv ed from a requesting p eer is sa v ed or ev en cac hed.
F urther, instead of sorting lists based on sp ecific IP addresses, a more generalized
form based on net w ork prefixes, e.g., /24 prefixes, could b e used. T o protect against
Distributed Denial of Service (DDoS) attac ks when requests originate from sp o ofed
IP addresses, the Oracle can limit the n umber of replies that are sen t to a sp ecific
IP address. A dditionally , since the size of the resp onse sen t bac k b y the Oracle to
a requesting p eer, is the same as that of the request that the p eer sends, it can not
b e used for amplification attac ks.
The monstrous demand and consumption of high qualit y media con tents has o v er-
tak en the use of P2P and is causing a ma jor shift from P2P to CDNs. The Oracle
service offers h uge p otentials that go beyond the scop e of P2P . It can easily b e
adapted to suit collab orations with CDNs as w ell.
On the one hand, CDNs w an t to deliv er con ten ts to end-users in the fastest an d most
efficien t w ay possible, but sit at the source and ha v e no con trol o v er the path that
requested con ten ts tak e to reac h end-users. On the other hand, ISPs pro vide the
paths used to deliv er these con tents, but lac k con trol o v er their sources. A lot is at
stak e for all parties in volv ed, i.e. the CDNs, the ISPs and the end-users. The Oracle
services pro vides the fundamen tal building blo c k for a suitably adapted solution in
this case, as w ell. In fact, researc hers ha v e adapted and extended the principles of
the Oracle to enable ISP-CDN collab oration [ 72 , 161 ] as w ell as to enable Con ten t-
a w are T raffic Engineering for traffic originated b y CDNs [ 71 ]. This has ev en ev olv ed
in to finished pro ducts (BENOCS Director [ 76 ] and BENOCS Analytics [ 75 ]) and
has resulted in the creation of a business Start-up, whic h is in-c harge of further
dev eloping and commercializing the pro ducts. Some global Tier1 ISPs and CDNs
are already on-b oard.
The Oracle service is also a ma jor con tributor to the standardized AL TO proto col,
created through an initiativ e of the IETF AL TO W orking Group. The In ternet draft
“The PR O XIDOR Service” [ 7 ] based on the Oracle Service is in tegrated in the final
AL TO proto col [ 10 ] in RF C7285.
4.9 Summa ry
It is no doubt that new trends and phenomena will con tin ue to emerge on the
In ternet. It is also no doubt that, when the prop ortion of traffic they generate
b ecomes quite significan t, net w ork op erators and researc hers will b ecome in terested.
Their effects on ISP top ologies and traffic managemen t approac hes will w arran t
detailed studies and (if need b e) appropriate mitigation approac hes. Recen tly , P2P
has w arran ted appropriate approac hes to deal with the effects of its enormous traffic
prop ortions.
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4.9 Summary
In this thesis and in this c hapter, we outlined some of the issues posed by P2P
systems and P2P traffic. W e therefore prop osed a solution that w e think should
address them to the b enefits of b oth the ISP and the P2P end-users. Our prop osal
is based on the use of a simple ISP-op erated v alue-added service, the Or acle service,
whic h helps p eers mak e informed and b etter decisions.
T o assess the b enefits of the Oracle service, w e implemen t the Gn utella P2P proto col
in a pac k et-lev el sim ulator, then design and mo del AS/P2P top ologies, with whic h
v arious asp ects of the P2P and ISP collab oration are studied. T o demonstrate the
adv an tages of using the Oracle service, w e p erform comparativ e sim ulation studies
and analyze their results. W e use a visualization tec hnique to sho w that the ov erla y
top ology b ecomes more aligned with the underla y top ology , when the Oracle service
is used and also that the o v erla y graph remains connected. Despite sligh t increases
in a v erage o v erla y graph diameter and sligh t decreases in the a v erage no de degree,
w e still record a substan tial increase in in tra-AS connections in the Oracle-biased
top ologies.
The user exp erience also impro v es, as eviden t in the recorded a v erage n um b er of
query resp onses and the a v erage do wnload times, which are significan tly b etter for
the Oracle-biased cases. W e also observ e h uge reductions in inter-AS o v erhead
traffic. In some top ologies, the biased inter-AS traffic is reduced to as little as one-
sixth and one-ten th of what they are in the un biased case, whic h is a 6-fold and
10-fold reduction, resp ectiv ely .
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5
T raffic Effects on Different Backb one
T op ologies
W e p erform c omp ar ative studies in this chapter thr ough evaluation of network and
applic ation p erformanc es in thr e e differ ent b ackb one top olo gies under high tr affic
lo ad. A l l thr e e top olo gies have the same numb er of no des, but differ fr om e ach other
by the numb er of links (i.e. 66, 30 and 20 r esp e ctively) c onne cting these no des. They
also differ with r esp e ct to the total c ap acity in the top olo gy and in the natur e of their
inter c onne ctions. W e also study their r esp e ctive r esp onses to an incr e ase d tr affic
lo ad of 35%, as wel l as, to a single link failur e, i.e. when the link with the highest
thr oughput in e ach top olo gy fails.
New trends and c hanges in user b eha vior can affect traffic div ersit y , v olumes and
flo ws in v arious w a ys. Netw ork Pro viders adapt b y re-engineering their infrastruc-
tures to accommo date the effects that accompan y such trends and c hanges. T o
study ho w the top ology of bac kb one net w orks affect traffic flows and o v erall net-
w ork/application p erformance, w e use a reference bac kb one net w ork mo del for Ger-
man y [ 17 , 93 , 95 ]. W e mo dify the n umber of no des to 12 (in accordance with a more
recen t reference mo del (IDEALIST pro ject [ 39 ]), then v ary the num b er of links and
the no des they in terconnect, to obtain 3 dissimilar top ologies. W e then apply similar
loads to eac h top ology and analyze their p erformances based on v arious criteria.
5.1 Backb one T op ologies
T op ology design is an essential part of the traffic managemen t solution. Long-
haul bac kb one transp ort net w orks, whic h are generally c haracterized b y v ery high
construction and op erational costs, are also mostly static in nature. Once designed
and built, they main tain the same structure for decades, desp ite latest conditions
that necessitate a more flexible top ological structure, which can easily and flexibly
con tain the gro wing and increasingly more dynamic traffic volumes and flo ws. ISPs
instead turn to in v est more on faster and bigger routers/links than in redesigning the
arc hitecture of the underlying transp ort netw ork. Easier top ological c hanges, such
as links addition/remo v al/mo v es are ho w ev er more probable and often preferred.
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
Figure 5.1: German y Bac kb one T op ologies
T able 5.1: Summary of Bac kb one T op ologies
Figure 5.2: No de Degree Distribution
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5.2 IP T raffic Demand Matrix
F or our studies, w e mo del one full-mesh and t wo partial-mesh topologies. The no des
represen t P oin ts of Presence (P oP) or ma jor Data Cen ter (DC) lo cations. The in-
terconnections b et ween the no des represen t national bac kb one links. The in tercon-
nections and no de degree distribution v ary from top ology to top ology . Figure 5.2
sho ws the no de degree distributions in eac h top ology . The higher the no de-degree,
the b etter connected that no de is. Eac h top ology also differs from the others in
terms of their total bandwidth, as can b e seen in T able 5.1 .
5.1.1 The F ullmesh T op ology
All P oPs in the first top ology are directly in terconnected with eac h other, constitut-
ing a full-mesh. This is also the reason wh y w e’v e named it the F ul lmesh top ology .
The F ullmesh top ology thus comprises 12 P oPs and 66 links. Eac h P oP has a no de-
degree of 11, which is the a v erage no de degree for this top ology , as w ell as the highest
p ossible no de degree for this and the other 2 top ologies.
5.1.2 The 30-Links T op ology
The 30-Links top ology forms a partial-mesh that connects the 12 P oPs using 30
bac kb one links. A little less than half as m uch links as are presen t in the F ullmesh
top ology . A unique feature of this top ology is that, one P oP (F rankfurt), from
where the largest v olume of traffic to other P oPs is sourced, also connects via direct
bac kb one links to eac h of the other 11 P oPs. Th us, the no de degree of the F rankfurt
P oP is 11, while that of the other P oPs v ary b et w een 2 and 8. The a v erage no de
degree in the top ology as a whole is 5.
5.1.3 The 20-Links T op ology
The 20-Links top ology also forms a partial-mesh. Ho w ev er, it has the least n u m b er of
bac kb one links b et w een its 12 P oPs an d no unique feature, suc h as direct connections
b et ween top talking P oPs. The no de degree in this top ology v aries b et w een 2 and
5, resulting in an a verage node degree of only 3.3, whic h is also the low est a v erage
v alue of all three top ologies.
5.2 IP T raffic Demand Matrix
The IP traffic demand matrix represen ts the v olume of IP traffic that flows b et w een
eac h Origin-Destination (OD) pair in the top ology . W e use a traffic demand matrix,
whic h represen ts p eak hour load captured at 15-min utes in terv al. Demand matrices
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
that are captured at shorter time in terv als, e.g. of 10 or 5-min utes, are more accu-
rate and more sensitiv e to load v ariations. Ho w ev er, w e assume minimal OD load
v ariation during the p eak hour and therefore consider the 15-min utes interv al to b e
ok for our purp oses.
5.3 Simulation Studies
As men tioned in section 5.1 , the top ology of a netw ork is an imp ortan t design
elemen t with far reac hing implications on its p erformance. T o ev aluate these effects,
w e design and run p erformance-based exp erimen ts on each of the three represen tativ e
top ologies describ ed ab o v e. W e then use selected metrics to analyze and compare
their p erformances under the same net w ork conditions.
5.3.1 The OPNET Mo deler Simulato r
Exp erimen ts on (or inv olving) bac kb one net w orks are rarely p ossible on real net-
w orks b ecause of the high risk of impacting pro duction and the serious implications
that could follo w from that. Researc hers and op erators resort to sim ulations for
suc h studies. An appropriate sim ulator needs to b e c hosen, one that p erfectly meets
the conditions and requiremen ts of the system to b e studied.
After ev aluating some of the most p opular net work sim ulation to ols, w e decided
to settle with the Optimized Net w ork Engineering T o ol (OPNET) [ 111 ]. OPNET
Mo deler 1 is a pac k et-lev el ev en t-based net w ork mo deling, simulation and analysis
to ol that pro vides man y adv an tages that most of the other to ols do not offer. These
include its comparably v ery high sim ulation sp eed, the p ossibilit y to simulate v ery
large comm unication net w orks using a detailed library of editable mo dels that sup-
p ort existing proto cols, an extensive list of m ulti-v endor mo dules and libraries, as
w ell as in terfaces for plug-ins. It also allows the design and study of net w orks,
devices, proto cols and applications with great flexibility . It is widely used in the re-
searc h comm unity and b y net w ork op erators for planning, analysis and p erformance
ev aluations [ 178 ].
5.3.2 System Design
Eac h top ology comprises 12 PoPs, re presen ting 12 ma jor German cities, and a pre-
determined n um b er of bac kb one links. The num b er and size of the in terconnections
within eac h top ology are giv en in T able 5.1 . These bandwidths are mo deled to
suit the sim ulation-en vironment, suc h that 2.5Gbps sim ulated bandwidth represents
1 OPNET Mo deler is no w called Riv erb ed Mo deler [ 198 ]
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5.4 Net w ork Performance Analyses
10Gbps in real and 10Gbps sim ulated bandwidth represen ts 40Gbps in real, resp ec-
tiv ely . The link dela ys are based on measured a v erages or calculated from fib er
lengths [ 32 , 184 ].
The n um b er of users (or clien ts) is the same (180) in all 3 top ologies. These clien ts
are distributed among the 12 P oPs, in prop ortion to their resp ectiv e traffic demands
and as sho wn in T able 5.2. They serv e as originators of the differen t requests for
applications/services a v ailable via connections to remote DCs. Only connections
and traffic to/from remote DCs are considered and implemen ted b ecause of their
relev ance in our studies. Lo cal connections and traffic to/from lo cal serv ers, i.e.
those that do not cross a bac kb one link, are completely left out or ignored in our
analyses.
All application serv ers in the DC are designed to b e large enough, in terms of
pro cessing p o w er and sp eed, to enable them resp ond to all clien t-requests in an
effectiv e and timely manner. This is necessary , to a v oid un w an ted b ottlenec ks that
could impact the results in our studies. The t yp e and n um b er of application serv ers
is uniform across all DCs (P oPs).
The bac kb one router is uniform across all PoPs. Op en Shortest P ath First (OSPF)
is used as the IGP routing proto col of c hoice 2 . OSPF’s adv anced features, such as
b etter (top ology-dep enden t) metrics and equal-cost load-balancing also pla y critical
roles in our studies.
5.3.3 T raffic Mo del
T w o types of traffic are realized in the system. The first t yp e is deriv ed from a traffic
demand matrix, a necessary input for suc h studies, and serv es as the bac kground
traffic b et ween all OD P oP pairs. The second t yp e of traffic comes from mo deled
applications, p ossessing pac k et-level details and generated as a result of in teractions
b et ween the clien ts and serv ers [ 15 , 43 , 85 , 178 ].
The same bac kground and application traffic v olumes and flows (traffic demand
b et ween all SD pairs) are used in eac h of the three top ologies. Ho w ev er, the path
and hence the p erformance of eac h flo w, whic h is a function of the top ology through
whic h it flo ws, v aries according to the prop erties of that particular top ology .
5.4 Net w o rk P erfo rmance Analyses
The used traffic demand matrix represen ts the a ver age of that measured during
the p eak (or busy) hour. T o em ulate this p eak hour condition, w e also run all
2 Although, w e decided to stick with OSPF, it should be noted that initial exp erimen ts were also
done with In termediate System - In termediate System (IS-IS), another link-state IGP similar to
OSPF, with v ery similar results.
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
T able 5.2: Summarized T raffic Matrix (including clients distribution p er lo cation)
exp erimen ts for 3600 seconds. W e ensure that all services and applications start only
after routing con v ergence has o ccurred in the top ology , whic h is t ypically within the
first 10 seconds, but not later than the first 100 seconds.
F our imp ortant performance scenarios are studied:
• P erformance under baseline traffic condition
• P erformance under baseline traffic but in the presence of a single link failure
• P erformnace when baseline traffic increases b y 35%
• P erformance when traffic increases b y 35% plus a single link failure
Global p eak hour In ternet traffic is exp ected to gro w 4.6-fold from 2016 to 2021 at
a comp ound ann ual growth rate of 35% [ 108 ]. This is tak en in to consideration in
our design and is the reason wh y w e also select 35% as the rate for traffic gro wth in
all three top ologies.
F or failure analyses, the link with (i) the highest throughput and (ii) the highest
utilization in eac h of the 3 top ologies is selected. There are v arious reasons for link
failures, e.g. fib er cuts, p ort failure, router malfunction, etc. Our study do es not
dw ell on these causes, but rather on their general effect, i.e. when the link (for
whatev er reason) suddenly b ecomes una v ailable (or fails).
In their analytical study of link failures in an op erational IP backbone netw ork,
Iannaccone et al observ ed that 10% of link failures last longer than 20 min utes,
while 40% last b et ween one and 20 min utes and 50% last less than one min ute [ 97 ].
F or failure analyses, w e therefore consider a single link failure scenario in our mo dels
and select a realistic failure duration of 10 min utes during a t ypical p eak hour.
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5.4 Net w ork Performance Analyses
5.4.1 P ack ets Hop Count
The hop coun t is the total n umber of IP lay er devices (routers) that a pac k et go es
through b efore reac hing its final destination. W e extrap olate the hop coun t of all
successfully receiv ed pac k ets, as sho wn in T able 5.3 , then analyze them p er top ology
and scenario.
T able 5.3: P ack ets Hop coun t (Receiv ed traffic)
Figure 5.3: A v erage pack et Hop Coun t at baseline traffic
As sho wn in Figure 5.3 , pac k ets in the F ullmesh top ology tra v el the least distances
to get to their final destinations. This is an attribute (and adv an tage) of a fullmesh
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
top ology , since access to all other remote P oPs is p ossible via directly connected
links. T o go from a source to a final destination, pac k ets that are exc hanged b et w een
end-systems need to tra v el a single hop to their lo cal gatewa y router, then a single
hop across the bac kb one net w ork and one more hop from the remote router to
their ultimate destination. This equates to only 3 hops for an y clien t/serv er remote
comm unication in the F ullmesh top ology .
Figure 5.4: A v erage pack et Hop Coun t at 35% increased traffic
5.4.2 Link Throughput and Utilization
The throughput of a link is a measure of the amoun t of data that is successfully
transmitted via the link in bits/s. Link utilization is the ratio of its used band-
width to its maxim um bandwidth, expressed as a p ercen tage. Since our top ologies
ha v e links of differen t bandwidths (10Gbps and 2.5Gbps resp ectiv ely) and since a
ma jor goal of an y net w ork is to push as muc h traffic through as p ossible, w e use
throughput instead of utilization as our measuremen t and comparison criteria. W e
therefore consider the p erformance of the top 10 links in each topology , with resp ect
to throughput.
F ullmesh T op ology
Under baseline traffic condition, the a verage throughput of the top 10 links in the
F ullmesh top ology ranges only b et ween 1.25Gbps and 4.04Gbps, as sho wn in Figure
5.5 a. This comparativ ely lo w er throughput is also accompanied b y comparativ ely
lo w link utilizations, resulting from the high div ersit y of direct and indirect paths
of comparable bandwidths and metrics b et w een an y t w o P oPs in the top ology . The
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5.4 Net w ork Performance Analyses
high v olume of traffic is th us distributed among these links, such that, the a v erage
load and utilization on individual links remains lo w.
When the traffic in the top ology increases b y 35%, the a verage throughput also
increases to a range b et w een 1.68Gbps and 5.45Gbps resp ectiv ely , as can b e seen in
Figure 5.5 b. The same top 10 links are main tained, how ev er, the ranking b et w een
the 8th-placed link (D - DO → ) and the 9th-placed link (HH - D ← ) in the baseline
traffic scenario, is switc hed in the increased traffic scenario.
The F rankfurt-to-Munic h link (M - F ← ) has the highest throughput amongst all
links in this top ology , in the baseline traffic scenario, as w ell as in the 35% increased
traffic scenario. When this link fails at baseline traffic lev el, its flo ws are div erted
from F rankfurt through Hano ver to Munic h, causing the a v erage throughput on the
F rankfurt-to-Hano ver link (H - F ← ) to rise from 2.9Gbps and p eak at 7.1Gbps, as
can b e seen in Figure 5.5 c. In the 35% increase traffic scenario, the same link failure
causes the traffic to b e rerouted from F rankfurt through Stuttgart to Munic h, raising
the a v erage throughput on the F rankfurt-to-Stuttgart link (S - F ← ) from 1.7Gbps
to its p eak at 7.3Gbps, as sho wn in Figure 5.5 d. When the failed link is restored,
the a v erage throughput in b oth scenarios return to their pre-failure lev els.
30-Links T op ology
In the 30-Links top ology , the a v erage throughput under baseline condition, ranges
from 1.47Gbps to 4.18Gbps for the top 10 links. Ho w ever a clear gab is observ ed
b et ween the bottom 3 of the 10 links that range b et w een 1.47Gbps and 1.5Gbps, and
the rest of the 7 links, whic h are b et w een 2.7Gbps and 4.18Gbps marks, as sho wn
in Figure 5.6 a.
A traffic increase of 35% causes the a v erage throughput to rise to a range b etw een
1.96Gbps and 5.65Gbps, for the top 10 links, as depicted in Figure 5.6 b. A similar
gab b et ween the bottom 3 and the top 7 of these links is observ ed in this scenario as
w ell. Ho w ev er, the gab has gro wn sligh tly , from 1.21Gbps in the baseline scenario
to 1.64Gbps in the increased traffic scenario.
In the second top ology , the F rankfurt-to-Munic h link (M - F ← ) is still the link
with the highest throughput, b oth during baseline traffic, as well as when the traffic
increases b y 35%. This is therefore the link that is c hosen for the single-link failure
analysis. When this link fails during baseline traffic, its throughput drops to zero,
white that of the neigh b oring F rankfurt-to-Stuttgart link (S - F ← ) sho ots up from
2.93Gbps to 7.06Gbps, which is more than double the v alue b efore the failure. Other
top 10 links are also affected and are observ ed to ha ve lo w er throughputs during the
failure. After the failed link is restored, its throughput returns to v alues sligh tly
higher than what they w ere b efore the failure. The neigh b oring (S - F ← ) link also
returns to v alues sligh tly higher than the ones it had b efore the failure. How ev er,
not all of the top links are restored to v alues at or ab o ve their pre-failure lev els. The
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
Figure 5.5: A v erage link throughput in F ullmesh top ology (top 10)
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5.4 Net w ork Performance Analyses
throughput of a few of the top 10 links increase sligh tly , but not to their pre-failure
lev els, while others remain unchanged, ev en after the failed link is restored. These
observ ations can b e seen in Figure 5.6 c.
The effect of the same link failure at 35% increased traffic is a bit differen t and more
in tensified on some of the top 10 links. F or example, when the F rankfurt-to-Munich
link (M - F ← ) fails at 35% increased traffic, most of its traffic is div erted via a
differen t neigh b oring link, the F rankfurt-to-Leipzig link (L - F ← ), whic h is also the
functioning link most affected b y the failure. Its throughput gro ws from 2.93Gbps
to a p eak at 7.11Gbps during the failure. This 4.18Gbps c hange in throughput is
comparable to the 4.13Gbps c hange exp erienced b y the most affected link (S - F ← )
in the baseline traffic scenario.
20-Links T op ology
Of the 3 top ologies in this study , the 20-Links top ology has the least n um b er of
links, as well as the least coun t of the faster 10Gbps links. Under baseline traffic
condition, the top 10 links record throughput v alues that range from 2.46Gbps to
7.64Gbps. Figure 5.7 a sho ws that these are sub-divided in to 3 sub-ranges, with the
6 of the top 10 links in the lo w range that go es from 2.46Gbps to 3.14Gbps, 2 in the
mid-range that lies b et w een 5.1Gbps and 5.2Gbps and 2 in the top range, whic h is
b et ween 7.26Gbps and 7.64Gbps.
When the traffic in the top ology increases b y 35%, the av erage throughput of the
top 10 links also increases to a range b et w een 3.34Gbps and 9.5Gbps (whic h is the
maxim um p ossible throughput, b ecause of the OC-192 sp eed of the link). The 3 sub-
ranges still exist, but are no w b et w een 3.33Gbps and 4.24Gbps for the lo w range,
6.86Gbps and 7.04Gbps for the t w o links in the mid-range and 9.5Gbps for the tw o
links at the top-range, as is sho wn in Figure 5.7 b. W e notice a drop in a v erage
throughput on the Nurem b erg-to-Munich link (N - M → ), whic h is attributed to
congestion and excessiv e queuing dela y on the F rankfurt-to-Nurem b erg link, for
pac k ets from F rankfurt destined for Munic h (see Sections 5.4.3 and 5.4.4 ).
F or the single link failure analyses, w e use the F rankfurt-to-Nurem b erg link (N -
F ← ), since it has the highest throughput during baseline traffic, as w ell as when
traffic increases b y 35%. The failure of this link during baseline traffic causes a spik e
in the a v erage throughput of the neighboring F rankfurt-to-Leipzig link (L - F ← )
that go es immediately from 2.68Gbps to its maxim um at 9.5Gbps. After the failed
link is restored, its av erage throughput increases to v alues a bit higher than what
they w ere b efore the failure, while those of the F rankfurt-to-Leipzig link fall bac k to
v alues a bit lo w er than what they w ere b efore the failure.
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
Figure 5.6: A v erage link throughput in 30-Links top ology (top 10)
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5.4 Net w ork Performance Analyses
Figure 5.7: A v erage link throughput in 20-Links top ology (top 10)
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
5.4.3 TCP Dela y
The TCP dela y pro vides the time lapse b et w een sending a TCP datagram at the
source no de and receiving it at the destination no de. This time is recorded for all
TCP transactions in eac h top ology . Figure 5.8 sho ws the a v erage TCP dela y p er
top ology , for all three top ologies and all 4 scenarios.
Figure 5.8: A v erage TCP Delay
The a v erage TCP dela y 3 is mostly b elow the 25ms mark for all 3 topologies during
baseline traffic, see Figure 5.8 a. How ev er, in all 4 scenarios, it remains sligh tly
b etter (and visibly lo w er) in the F ullmesh top ology than in the 30-Links and 20-
Links top ologies.
3 In all 4 scenarios, the p eak a verage TCP dela ys at the start of the exp erimen t are not con-
sidered, since they are recorded when som e pro cesses are still initializing. This applies to all
measuremen ts in this study .
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5.4 Net w ork Performance Analyses
The effects of limited bandwidth in the 20-Links top ology , compared with the other
t w o top ologies, b ecomes ob vious when the traffic increases b y 35%. Its a v erage TCP
dela y starts rising from the b eginning up to ab out 1.4secs, then drops to ab out 213ms
at the 2300s time-line b efore rising again from there till the end of the simulation.
This b eha vior is attributed to queue buildup and release on t w o saturated links in
the top ology , b oth of which are completely maxed out in terms of throughput and
utilization.
The TCP dela y in the F ullmesh and 30-Links top ologies remain mostly unaffected b y
the increased traffic, but v ariably affected b y the link failure. A t baseline traffic the
failure affects the dela y in b oth top ologies equally . A t increased traffic, the dela y in
30-Links top ology is m uch more affected than that in the F ullmesh top ology , which
sho ws only minor impact.
5.4.4 TCP Retransmissions
A TCP retransmission o ccurs when a sen t TCP segment is lost (i.e. not confirmed b y
the destination host) and therefore needs to b e resen t. Con tin uous retransmissions
are an indication of congestion in the path from the source no de to the destination
no de.
Figure 5.9: TCP Retransmissions in 20-Links top ology
A t baseline traffic, there are zero TCP retransmissions in all 3 top ologies. When
traffic in the top ology is increased b y 35%, no TCP retransmission is recorded in
the F ullmesh and 30-Links top ologies. P er con tra, a significan t n um b er of TCP
retransmissions are recorded in the 20-Links top ology as a result of the increased
load.
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
When the link failure o ccurs, the num b er of retransmissions recorded in the F ullmesh
and the 30-Links top ologies are negligibly small (11 and 32 resp ectiv ely). These are
also recorded as one-time-ev en ts that o ccur once, immediately after the failure. In
con trast, the n um b er of retransmissions in the 20-Links top ology resulting from the
link failure is m uc h higher and contin ues ev en after the failed link has b een restored.
Figure 5.9 sho ws the n umber of TCP retransmissions resulting from congestion at
35% increased traffic and link failures in the 20-Links top ology . The kno c k-on effect
of congestion b ecomes noticeable in the later section of the increased traffic scenario,
when the n um b er of retransmissions go es from zero all through to a maxim um of
nearly 2500 at the end of the run.
5.4.5 RTP Dela y
R TP dela y records the time difference b et w een the time when an R TP pac k et is
timestamp ed at the source no de and the time it is receiv ed at the destination no de.
Figure 5.10: A v erage R TP Dela ys
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5.5 Application P erformance Analyses
The dela y is recorded for v oice pack ets in eac h top ology . Figure 5.10 a shows that
at baseline traffic, the a verage R TP dela ys in all three top ologies remain relativ ely
lo w and appro ximately constant o v er time. Ho w ev er, the b est a verage v alues of w ell
b elo w 4ms are recorded in the F ullmesh top ology , follo wed b y the 5.0ms and 5.3ms
a v erages in the 30-Links and 20-Links top ologies resp ectiv ely .
When traffic in the top ology increases b y 35%, the a v erage R TP delay in the 20-
Links top ology no longer remains constan t. It rises righ t from the start, reaching
a maxim um of 151ms b efore dropping to 50ms and then rising again from there to
ab out 139ms at the end of the simulation run. As can b e seen in Figure 5.10 b,
the resp ectiv e constant and lo w er a v erage dela ys in the F ullmesh and the 30-Links
top ologies are main tained, despite the increased traffic load.
Link failure has adv erse effects on the a verage R TP dela y in the 20-Links top ology ,
as can b e seen in Figures 5.10 c and 5.10 d resp ectiv ely . The failure at baseline traffic
causes a sharp increase in a v erage R TP dela y from a few milliseconds to a maxim um
of appro ximately 144ms. This falls bac k to normal v alues immediately after the
link is restored. In the 30-Links top ology , on ly a sligh t increase in dela y o ccurs, as
a result of the link failure. This also falls bac k to normal after the failed link is
restored. Only negligible c hanges are observ ed in the F ullmesh top ology . Neither
the link failures nor the increased traffic in the top ology sho ws any significan t effect
on the dela y v alues.
5.5 Application P erfo rmance Analyses
In this section, w e analyze and compare the p erformance of some standard applica-
tions, with regards to traffic conditions in eac h of the 3 selected top ologies.
5.5.1 FTP Do wnload Resp onse Time
The FTP Do wnload resp onse time is the timespan b et w een sending an FTP request
to a serv er and receiving the complete resp onse pac k et from it. The a v erage resp onse
time in eac h top ology is shown in Figure 5.11 . A t baseline traffic, Figure 5.11 a sho ws
only sligh t difference b etw een the a v erage do wnload times in eac h of the 3 top ologies.
Ho w ever, when link failure o ccurs, Figure 5.11 c sho ws a h uge increase in the a verage
do wnload time in the 20-Links top ology , compared to only sligh t c hanges in the
30-Links and F ullmesh top ologies.
When traffic in the top ology increases b y 35%, Figure 5.11 b sho ws that the a v erage
do wnload times in the 30-Links and F ullmesh top ologies remain con tin uously lo w,
while that in the 20-Links top ology increases from the start to a maximum of 49.98
seconds, after 2232 seconds of sim ulation. It then drops to 5.23 seconds shortly
thereafter, b efore slo wly rising again with time.
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
Figure 5.11: A v erage FTP Download Response time
Link failure at this increased traffic load, only minimally affects the do wnload times
in the F ullmesh top ology , as can b e seen in Figure 5.11 d. The a v erage FTP do wnload
time in the 30-Links top ology clearly increases as a result of the failure, while that of
the 20-Links top ology first drops and then starts increasing again during the failure
p erio d. It drops again after the link is restored and then rises again immediately
thereafter.
5.5.2 HTTP Received T raffic
In all three top ologies, we observ e that the amoun t of HTTP traffic that is receiv ed
in b ytes/s, closely corresp onds to the amoun t that is sen t 4 . A t baseline traffic all 3
top ologies send and receiv e approximately the same amoun t of HTTP traffic, as can
b e seen in Figure 5.12 a for receiv ed traffic only . Ho w ev er, when the total traffic in
the top ology increases b y 35%, a stark difference b et ween the topologies is observed.
Under this condition, m uc h less HTTP traffic is sen t and receiv ed in the 20-Links
top ology than in the F ullmesh and 30-Links top ologies (see Figure 5.12 b).
4 Since the amoun t of HTTP traffic receieved closely corresponds to the amoun t sen t, only the
amoun t receieved is plotted
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5.5 Application P erformance Analyses
Figure 5.12: A v erage HTTP T raffic Receiv ed
The effect of a single link failure on the quan tit y of HTTP traffic sen t and receiv ed in
eac h top ology , at baseline traffic, as w ell as at increased traffic, can b e seen in Figure
5.12 c and Figure 5.12 d resp ectiv ely . While b oth quan tities are virtually unaffected
b y the link failures in the F ullmesh and 30-Links top ologies resp ectiv ely , in the 20-
Links top ology and at baseline traffic, w e notice a significan t drop in the quan tit y
receiv ed during the link failure. When link failure o ccurs at increased traffic load,
the quan tit y of HTTP traffic receiv ed is sho wn to sligh tly increase and then slo wly
drop again with time.
5.5.3 HTTP Object Resp onse Time
The HTTP ob ject resp onse time is the time tak en b y a bro wser (or an HTTP clien t)
to retriev e an HTTP em b edded ob ject from an HTTP serv er. All HTTP transactions
are tak en in to consideration when calculating the av erage ob ject resp onse time in a
giv en top ology .
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
Figure 5.13: A v erage HTTP Ob ject Resp onse time
A t baseline traffic, Figure 5.13 a sho ws a similar a v erage HTTP ob ject resp onse
time of appro ximately 12 milliseconds in the 20-Links and 30-Links top ologies. The
F ullmesh top ology records an a v erage of 10 millisecond, whic h is, 2 milliseconds
b etter than in the 20-Links and 30-Links top ologies. Figure 5.13 c how ev er sho ws
that, when the link failure o ccurs, the a v erage resp onse time in the 20-Links top ol-
ogy increases the most, to nearly 83 milliseconds. In the 30-Links and F ullmesh
top ologies, it increases only to 17 and 12 milliseconds resp ectiv ely .
A t increased traffic load, Figure 5.13 b and Figure 5.13 d, sho w that only the a v erage
HTTP ob ject resp onse time in the 20-Links top ology is adv ersely affected b y the
increased load and the link failure resp ectiv ely . The a v erage resp onse time in the
30-Links and F ullmesh top ologies remain lo w under the same conditions or are only
minimally affected b y the link failure.
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5.5 Application P erformance Analyses
5.5.4 V oice P ack et Jitter
V oice pac ket jitter is a measure of the v ariation in the dela y of receiv ed voice pac k ets.
If t w o consecutive pac k ets lea v e the source no de with time stamps t 1 & t 2 and are
pla y ed back at the destination node at time t 3 & t 4 , then:
j itter = ( t 4 − t 3 ) − ( t 2 − t 1 )
A negativ e jitter indicates that the time difference b et w een the pac k ets at the desti-
nation no de w as less than that at the source no de. The recommended tolerance for
one-w a y p eak-to-p eak voice pac k et jitter is 30ms or less [ 199 ].
A t baseline traffic, the jitters in all 3 top ologies are in the lo w nanosecond-range and
therefore negligible (Figure 5.14 a).
Figure 5.14: V oice Jitter
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
With increased traffic, the jitter in the F ullmesh and the 30-Links top ologies remain
virtually unc hanged in the nanosecond range, from start to end. An increase in jitter
is ho w ev er observ ed in the 20-Links top ology later in to the sim ulation. Although its
measured jitter has increased to an order of magnitude in the lo w er-10s of microsec-
onds, the v alues are still negligible when compared with the acceptable tolerance of
30 milliseconds. The risen jitter is nev ertheless still indicativ e of increased dela y (or
congestion) resulting from the increased load in the top ology .
All 3 top ologies sho w slight but negligible increase in the jitter during link failure.
Ho w ever only the 20-Links top ology seem to suffer from an after-effect, long after
the failed link has b een restored, as can b e seen in Figure 5.14 c and 5.14 d.
When the link failure o ccurs at increased traffic load, the jitters in the F ullmesh
and the 30-Links top ologies still remain in nanoseconds-range, while those in the
20-Links top ology also remain in the lo wer-10s of microseconds-range. The 35%
increase in traffic, combined with the failure of the top-most link in eac h of these
top ologies, do not seem to affect their jitters in an y negativ e w a y . This is supp orted
b y the graph in Figure 5.14 d, whic h sho ws that the v alues recorded for their jitters
alw a ys remain far b elo w the acceptable tolerance v alue of 30 milliseconds.
5.5.5 Video P ack et End-to-End Dela y
The Video P ac k et end-to-end dela y measures the time tak en to send a video appli-
cation pac k et from a source no de to a destination no de. Although av erage dela ys
migh t differ, dep ending on the ph ysical prop erties of the top ology , it is imp ortan t
that they remain constan t in v alue, for go o d viewing exp erience. Real-time in terac-
tiv e video tolerates pac ket end-to-end dela ys of up to 200ms and jitters of up to 50ms
[ 199 ] for High Definition (HD) flo ws. A t baseline traffic, the video pac k et end-to-end
dela y in all three top ologies ha v e constant a v erages of 4.5ms, 5.4ms and 5.9ms for
the F ullmesh, 30-Links and 20-links top ologies resp ectiv ely . These are quite go o d
v alues, as can b e seen from Figure 5.15 a.
When traffic in the top ology is increased, the video pac k et end-to-end dela y in
the F ullmesh and 30-Links top ologies still remain low while that of the 20-Links
top ology increases righ t from the start. This indicates the presence of congestion in
the 20-Links top ology (Figure 5.15 b).
Link failure at baseline traffic causes a noticeable increase in video pac k et end-to-
end dela y in all 3 top ologies (Figure 5.15 c). Ho w ev er, only the dela y in the 20-Links
top ology rises to 58 ms, whic h, though is higher than those in the other 2 top ologies
(b oth b elo w 20ms), is still w ell within the acceptable dela y tolerance for HD video
flo ws.
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5.6 Summary
Figure 5.15: Video P ac ket End-to-End Dela y
A com bination of increased traffic and link failure in the 20-Links top ology causes
the already high video pac k et end-to-end dela y v alue to first drop at the time of
the failure and then rise rapidly again un til restoration. Immediately after the
restoration, it first drops and then starts increasing. This rev eals a higher lev el
of congestion in the top ology , whic h affects the activ e path, as w ell as the bac kup
paths that the video pac k ets take when the primary link fails. In the 30-Links
top ology , the dela y increases the moment the link fails, but falls bac k to normal lo w
v alues immediately after the link is restored. The dela y in the F ullmesh top ology is
minimally affected b y the failure, as can b e seen in Figure 5.15 d.
5.6 Summa ry
T o study ho w different bac kb one top ologies affect the general p erformance in a net-
w ork, we design 3 differen t bac kb one top ologies using a national reference bac kb one
mo del for German y . W e k eep the n um b er and lo cation of the no des constan t but v ary
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Chapter 5 T raffic Effects on Differen t Bac kb one T op ologies
the n um b er and distribution of their in terconnecting links, to obtain 3 top ologies
that differ in structural c haracteristics and total bandwidth capacities. W e name
them F ullmesh (with 66 links), 30-Links and 20-Links top ology resp ectiv ely .
W e carry out sim ulation studies on all 3 top ologies, placing eac h of them under the
same net w ork/traffic conditions. These conditions are represen ted in the sim ulation
en vironmen t by the follo wing defined scenarios i) baseline traffic ii) baseline traffic
+ single link failure iii) 35% increased traffic iv) 35% increased traffic + single link
failure. W e use selected net w ork and application metrics to analyze and compare
the p erformances in eac h of the 3 mo deled top ologies and for eac h of the defined
scenario.
All 3 top ologies sho w comparative performance in only a few cases during base-
line traffic. Ho wev er, for man y of the selected metrics and traffic conditions, the
p erformances in the 30-Links top ology comes m uc h closer to those in the F ullmesh
top ology . Nev ertheless, clear differences b ecome ob vious when traffic in the top ology
increases b y 35% and/or when a link failure o ccurs. The 20-Links top ology is ob-
serv ed to offer the least p erformance of all 3 top ologies under these conditions, while
the b est p erformance in nearly all scenarios are recorded in the F ullmesh top ology .
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6
Flo w Optimization using Mixed-Integer
Programming (MIP)
In this chapter, we exploit the use of Mixe d-Inte ger Pr o gr amming to impr ove the
p erformanc e in the 20-Links top olo gy, which is observe d in the pr evious chapter,
to offer the le ast p erformanc e amongst the 3 that ar e c omp ar e d. Some se ctions of
its network ar e observe d to b e highly c ongeste d while others liter al ly have little-to-
no tr affic on them. W e think altering the distribution of lo ad in this top olo gy and
minimizing the maximum utilization on its links should le ad to p erformanc e im-
pr ovements. W e ther efor e investigate via simulations if this p ostulation holds true
under the state d c onditions. F or this, we employ flow optimization as a me ans of
efficiently distributing the tr affic lo ad in the network, without changing its ar chite c-
tur e or physic al top olo gy. W e plan, run and analyze two sets of exp eriments; one
set using OSPF interfac e c osts obtaine d via automatic (default) metric c alculations,
the other set using OSPF interfac e c osts obtaine d via flow-optimize d Mixe d-Inte ger
Pr o gr amming.
There are a go o d n um b er of w orks on optimizing in tra-domain routing w eigh ts to
ac hiev e desired traffic engineering goals [ 66 – 69 ]. In this thesis and particularly in this
c hapter, we aim at the same goals, but differ in our approac h and assumptions. While
most of the former w orks dep end on flo w-splitting and mostly linear programming
approac hes, w e follo w a more realistic approac h that tak es unsplittable “elephant”
flo ws in to consideration [ 27 ] and use MIP instead of LP , to enable us include b oth
linear and non-linear constrain ts in our problem form ulation.
6.1 MIP Problem F o rmulation
T o determine the metrics needed for optimized flo w in the net w ork, w e form ulate an
MIP problem with appropriate constrain ts.
W e consider the directed graph G = (V, E), where V represen ts a set of v ertices (or
P oPs) and E a set of edges (or links). The capacit y is giv en by c : E → R and the
demand matrix b y D : V × V → R , where D ( i, j ) denotes the demand flo wing from
P oP i to P oP j via the path P ij . The task is to maximize the bandwidth used in
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Chapter 6 Flo w Optimization using Mixed-In teger Programming (MIP)
the net w ork sub ject to the follo wing conditions: i) the OSPF routing and ii) edge
capacities are not violated. W e define the follo wing v ariables:
w e ∈ N OSPF w eigh t of edge e ∈ E
x P k
ij ∈ { 0 , 1 } decides whether path P k
ij ∈ P ij can b e used for transmission
f P k
ij ∈ R + amoun t of flo w (bandwidth) sen t from i to j in the k-th path
cost P k
ij ∈ R + OSPF cost of the k-th path from i to j
M maxim um OSPF w eight
c ( e ) capacit y of edge (or link) e
U max maxim um utilization on link
Our ob jectiv e is to minimize the maxim um utilization on the links in order to av oid
congestion.
minimiz e U max (6.1)
sub ject to the follo wing constrain ts:
cost P k
ij = X
e ∈ P k
ij
w e ∀ ( i, j ) ∈ V × V : ∀ P k
ij ∈ P ij (6.2)
min _ cost ij ≤ cost P k
ij ∀ ( i, j ) ∈ V × V : ∀ P k
ij ∈ P ij (6.3)
cost P k
ij − min _ cost ij ≤ (1 − x P k
ij ) · M ∀ ( i, j ) ∈ V × V : ∀ P k
ij ∈ P ij (6.4)
X
P k
ij ∈P ij
f P k
ij = D ( i, j ) ∀ ( i, j ) ∈ V × V (6.5)
f P k
ij ≤ D ( i, j ) · x P k
ij ∀ ( i, j ) ∈ V × V : ∀ P k
ij ∈ P ij (6.6)
U max ≥
P
( i,j ) ∈ V × V P
P k
ij ∈P ij : e ∈ P k
ij
f P k
ij
c ( e ) ∀ e ∈ E (6.7)
c ( e ) ≥ X
( i,j ) ∈ V × V
X
P k
ij ∈P ij : e ∈ P k
ij
f P k
ij ∀ e ∈ E (6.8)
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6.2 Solving the MIP Problem
6.2 Solving the MIP Problem
MIP problems are generally m uc h harder to solve than e.g. LP problems. The time
tak en to solv e an optimization problem dep ends on man y factors, suc h as the size
of the top ology , the n um b ers of in volv ed parameters, v ariables and constrain ts, as
w ell as the sp eed of the programming soft w are (solv er). Using mo dern computing
systems that ha v e ver y fast pro cessing units and large memories, the time needed to
solv e suc h problems could b e k ept relativ ely short. There are t w o ma jor categories of
solv ers a v ailable to users, i) free/op en-source solv ers and ii) commercial solvers. W e
started off using the GNU Linear Programming Kit (GLPK) [ 138 ], the most p opular
free solv er, but quic kly noticed it w as to o slow and could not solv e our problem in
reasonable time. W e th us chec k ed on the three most renounced commercial solv ers
for MIP , i.e. IBM CPLEX [ 41 ], FICO XPRESS [ 55 ] and Gurobi [ 155 ]. W e chose the
Gur obi Optimizer [ 154 ] b ecause of its sup erior sp eed compared with all the other
solv ers [ 141 , 142 ], its reputation within the industry and the fact that they offer a
free and full-featured academic license. With it, w e could obtain solutions within
min utes, compared to hours when using the free/op en-source solv ers.
6.3 Simulation Study
T o p erform the simulation studies in this c hapter, w e use the same 20-Links top ology
that is used in the previous c hapter and increase the traffic in the top ology b y 35%,
as is done in the increased-traffic scenarios of the previous c hapter. W e next design
and run t w o sets of exp erimen ts using tw o differen t routing sc hemes. One based
on automatically calculated cost metric (the default metho d), the other using MIP
to determine optimized cost metric v alues for eac h router in terface attac hed to an
activ e link.
The first set of exp erimen ts are based on default in terface costs. Their results are
denoted b y A UTO b ecause of the automatic calculation of the metric using the
default form ula:
In terface cost = Reference Bandwidth
In terface Bandwidth
W e use 100Gbps as our reference bandwidth instead of the default 100Mbps. This
is necessary to o v ercome the limitations of the default 100Mbps when dealing with
links of m uc h higher bandwidths. Leaving the reference bandwidth at 100Mbps
w ould yield the same metric v alue for all links with sp eed greater than or equal to
100Mbps, whic h is not appropriate for our purp oses.
The second set of exp erimen ts use in terface costs that are determined via the MIP
optimization pro cess. Its results are denoted with OPT . It should b e noted here
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Chapter 6 Flo w Optimization using Mixed-In teger Programming (MIP)
that the pro cess to determine the OPT cost metrics is done externally , in a Gurobi
standalone en vironmen t. The results are then collected and man ually en tered in to
the OPNET sim ulation en vironment. In a real pro duction en vironmen t, this task
could b e automated, using an appropriate Netw ork Managemen t System (NMS)
that, for example, ensures routing lo ops and disruptions are a v oided [ 70 ].
In b oth sets of exp erimen ts, neither the top ology nor arc hitecture is changed. The
only c hange in v olv ed is that of adjusting the OSPF routing metrics to influence the
paths that flo ws tak e.
W e sim ulate t w o scenarios eac h for the A UTO routing top ology and the OPT routing
top ology , resp ectiv ely . That is, a normal scenario (increased traffic without link
failure) and a failure scenario (increased traffic with single link failure). F or the
single link failure, the link with the highest throughput and utilization during normal
op eration (baseline traffic) is selected and failed for 10 min utes in the middle of the
sim ulation run, i.e. in the p erio d b etw een 1500 and 2100 seconds.
6.4 Results and Analyses
Since no top ological c hanges are inv olv ed in the scenarios studied in this c hapter, all
results and analyses will b e based on application and proto col p erformances only .
6.4.1 Throughput and Utilization
Although throughput and utilization are imp ortan t link p erformance and compar-
ison metrics, they tak e on differen t significance when analyzing/comparing links of
unequal capacities. In the case of backbone top ologies, throughput tak es on a more
imp ortan t significance b ecause of the need to push through the largest p ossible v ol-
umes of data across a net w ork at the highest p ossible sp eed. Utilization pla ys the
role of an indicator on individual links. It can b e used to signal (indicate) when
a link b ecomes saturated, w arranting an upgrade or a c hange of routing p olicy to
offload traffic from it.
W e compare the throughput and the utilization on the individual links in the top ol-
ogy when routing is based on A UTO in terface costs and OPT interface costs respec-
tiv ely . Both directions of flo w on a link are considered/analyzed separately (as in a
directed graph). T able 6.1 sho ws the throughput and utilization in b oth directions
of a link.
The results for the A UTO routing top ology are sho wn in T able 6.1 a. W e observe
that links with higher bandwidths are also those with higher throughputs. This is
exp ected, since the routing metric fa vors paths with high-bandwidth links o v er those
with lo w er-bandwidth links. The results for the OPT routing top ology are sho wn in
T able 6.1 b.
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6.4 Results and Analyses
T able 6.1: Link Utilization and Throughput at increased traffic
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Chapter 6 Flo w Optimization using Mixed-In teger Programming (MIP)
T able 6.2: Link Utilization and Throughput at increased traffic and single link failure
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6.4 Results and Analyses
The inclusion of additional constrain ts apart from only bandwidth when determining
the in terface cost, means that paths ha ving high-bandwidth links are not necessarily
preferred o v er those having lo w er-bandwidth links.
In addition, the optimization pro cess that also tak es in to consideration factors suc h
as limitation of the maxim um utilization on links and sharing of load across m ultiple
paths, can cause some lo wer-bandwidth links to experience higher throughputs than
some high-bandwidth links.
In terms of utilization, w e observe 2 o v er-utilized 1 links in the A UTO routing top ol-
ogy , as sho wn in T able 6.1 a. One from F to N and the other from F to H. In con trast,
there are no o v er-utilized links in the OPT routing top ology , as can b e seen from
T able 6.1 b, b ecause of the same reasons men tioned in the previous paragraph.
In the A UTO routing top ology we observ e a p o or distribution of traffic, with man y
links ha ving little or no traffic on them, while others are close to saturation or ev en
saturated. In the OPT scenario, w e observ e a b etter distribution of traffic across all
links.
With link failure w e observ e o v er-utilization on 6 links in the A UTO routing top ol-
ogy , as can seen in T able 6.2 a and only on 2 links in the OPT routing top ology , as
sho wn in T able 6.2 b.
6.4.2 P ack et Hop Count
Again, w e consider only pac k ets attributed to remote connections to/from P oPs
other than the lo cal P oP , i.e. pac k ets that are transmitted via backbone links.
T able 6.3: P ack ets Hop coun t at 35% increased traffic (20-Links T op ology)
T able 6.3 sho ws the num b er of hops that these pac k ets trav erse b efore arriving at
their final destination and the total coun t of pac k ets in eac h case. F or each top ology ,
b oth the normal, as well as the failure scenarios are sho wn. The same information
is presen ted as bar c harts in Figure 6.1 .
1 F or our exp eriments, w e consider utilizations up to 99.9% as normal. Any v alue ab o v e this limit
is considered o ver-utilization
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Chapter 6 Flo w Optimization using Mixed-In teger Programming (MIP)
Figure 6.1: P ac ket hop coun t at 35% increased traffic (Bar c hart)
First, w e notice that, while some pack ets in the A UTO routing top ology tra v el righ t
up to 9 hops and b ey ond, there are none in the OPT routing top ology that tra v el
b ey ond 7 hops, ev en when link failure o ccurs.
Although, in all scenarios, the pac ke t coun t decreases as the n umber of h ops in-
creases, for hops n umber 3 and 4, during normal op erations, w e observ e a large
difference in pac k ets-coun t b et ween the A UTO and the OPT top ologies, as sho wn
in Figure 6.1 a. In the presence of failure, this difference increases ev en more for hop
n um b er 3, while it reduces for hop n umber 4, as shown in Figure 6.1 b.
As w as observ ed in Section 6.4.1 and shown in T able 6.1 and T able 6.2 , there are
man y links in the A UTO top ology that ha ve little or no traffic on them. This is in
con trast to the OPT top ology , where there is a b etter distribution of traffic across its
links, lea ving virtually no link un used. F or the unused links in the A UTO top ology ,
the same links are sho wn to b e in use in the OPT top ology to transfer ev en more
data b et ween the P oPs on b oth ends of the links. This accoun ts for the observ ed
difference in the n um b er of pac k ets with lo w hop coun ts, esp ecially for hops 3 and 4
as can b e seen in Figure 6.1 .
6.4.3 TCP P erfo rmance
W e next analyze the p erformance of TCP , based on three criteria; the TCP dela y ,
the TCP segmen t dela y and the num b er of TCP retransmissions. F or eac h of these
criteria, w e analyze their p erformance under normal condition, as w ell as under link
failure condition.
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Figure 6.2: TCP P erformance
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TCP Dela y
Figure 6.2 a sho ws the a v erage TCP dela y under normal condition. W e notice quite
stable and lo w v alues in the OPT top ology , compared to v ariable and at times, muc h
higher v alues in the A UTO top ology . This can b e attributed to congestion on some
of the ma jor links in the A UTO top ology . A maxim um difference of 13.63 seconds
is recorded at 2736 seconds in to the 3600 seconds sim ulation. The dela y in the OPT
top ology at this time is only 21 milliseconds.
Link failure causes the a v erage TCP dela y in the OPT top ology to increase sligh tly ,
as can b e seen in Figure 6.2 b. Ho w ev er, after restoration, the v alues drop bac k
to their normal millisecond range. On the con trary , the a v erage TCP dela y in the
A UTO top ology drops as a result of the failure, then rises again after restoration.
This is an indication of a p o or distribution of traffic, suc h that, when the link fails
and new routes are calculated, under-utilized links are also exploited, leading to
b etter (lo wer) dela ys.
TCP Segment Dela y
TCP breaks do wns large application data pac k ets in to m ultiple segmen ts suitable
for transp ort across the net w ork, b efore sending them to their destination no des.
The a v erage TCP segment dela y is the mean dela y (in seconds) of segmen ts receiv ed
b y the TCP la y er in all no des and for all connections in the top ology . It is measured
from the time a TCP segmen t is sen t from the source TCP la yer to the time it is
receiv ed b y the TCP lay er in the destination no de.
With TCP segmen t dela y , the effect of congestion and the difference b et w een AUTO
routing and OPT routing b ecomes more pronounced, as can b e seen in Figure 6.2 c
and Figure 6.2 d, for the normal and link failure scenarios, resp ectiv ely .
TCP Retransmissions
TCP retransmission coun ts the n umber of times a TCP-segmen t ha v e had to b e
resen t b ecause a previous copy did not arriv e at its final destination. The n um b er of
TCP retransmissions is a go o d indicator of congestion and subsequen t pac ket-drops
in the net w ork. When a pac k et is dropp ed as a result of congestion, TCP reduces
its sp eed of transmission, then attempts to resend the dropp ed pac k et.
Figure 6.2 e sho ws no retransmissions in the OPT routing top ology during normal
op erations. In the A UTO routing top ology , all starts w ell, with zero retransmissions
in the first 500 seconds. Thereafter, the n um b er of retransmissions virtually increases
un til the end of the sim ulation. A p eak of 24008 retransmissions is reac hed at 3132
seconds in to the 3600-seconds sim ulation.
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6.4 Results and Analyses
When the link failure o ccurs, Figure 6.2 f sho ws that the n um b er of retransmission in
the OPT routing top ology , increases only sligh tly , from zero to a maxim um of 82. It
then falls bac k to zero immediately after the link is restored. This is negligible and
do es not adv ersely affect the TCP p erformance. In con trast, the link failure causes
the already high retransmission rate in the A UTO routing top ology to suddenly
drop. But then, the n umber of retransmissions starts increasing again shortly after
and ev en b efore the failed link is restored. A sharp er increase is also observ ed
immediately after restoration, whic h virtually increases all through un til the end of
the sim ulation run.
The observ ed increase in the n umber of retransmissions with time in the AUTO rout-
ing top ology and the recorded drop in this n um b er during failure, are further indi-
cators and confirmation of congestion and p o or traffic distribution in that top ology .
The go o d results in the OPT routing top ology additionally confirm the adv an tages
of optimization and the effectiv eness of the traffic redistribution.
6.4.4 FTP P erfo rmance
W e next lo ok at the FTP p erformance, with resp ect to the amoun t of traffic sen t
and receiv ed, as w ell as to the recorded download response time.
Figure 6.3 a sho ws the time-a v erage of the FTP traffic that is sen t in the A UTO
routing and the OPT routing top ologies during normal conditions. With a few
exception in the first 500 seconds of the sim ulation, where the a v erage FTP traffic
sen t (in b ytes/s) b y FTP serv ers to requesting FTP clien ts, app ear to b e sligh tly
higher in the OPT routing top ology than in the A UTO routing top ology , the amount
sen t thereafter is appro ximately the same in b oth top ologies. When failure o ccurs,
Figure 6.3 b sho ws only minor (A UTO routing top ology) to negligible (OPT routing
top ology) c hanges in the scenarios resp ectiv ely .
The FTP traffic receiv ed during normal and failure conditions resp ectiv ely , sho w
clear difference b et ween the amoun t receiv ed in the A UTO routing top ology and
that receiv ed in the OPT routing top ology . In the A UTO routing top ology m uc h
less traffic is receiv ed than w as sent (Figure 6.3 c) and the impact of link failure in
the top ology is also clearly affects the amoun t b eing receiv e at that time (Figure
6.3 d). W e observ e a sligh t increase in receiv ed traffic in the A UTO routing top ology ,
resulting from the link failure. On the con trary , Figures 6.3 c and 6.3 d resp ectiv ely
also sho w that the amoun t of FTP traffic receiv ed in the OPT routing top ology is
practically the same amoun t that w as sent and this amoun t is also not impacted b y
the link failure.
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Chapter 6 Flo w Optimization using Mixed-In teger Programming (MIP)
Figure 6.3: FTP p erformance
The a v erage FTP do wnload resp onse times in the A UTO and OPT routing top olo-
gies are sho wn in Figure 6.3 e, for normal op eration and Figure 6.3 f in the presence
of link failure. During normal op eration, w e observ e a v ery stable and lo w do wnload
resp onse time in the OPT routing top ology . On the con trary , the a v erage do wnload
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6.4 Results and Analyses
resp onse times in the A UTO routing top ology increase righ t from the start and is
quite dynamic. The v alues v ary with time, reac hing a maxim um of 165.9 seconds.
This is a great con trast to the maxim um of only 1.04 seconds measured in the OPT
routing top ology .
Figure 6.3 f sho ws in teresting traits of the recorded FTP do wnload resp onse time,
resulting from link failure. During the failure p erio d, the do wnload resp onse time
in the A UTO routing top ology remains high at first, then falls to very lo w v alues
b efore rising again after the link is restored. The maxim um (w orst) v alue of 173.85
seconds is recorded shortly after the failure, at 1548 seconds into the exp erimen t.
In the OPT routing top ology , the failure also causes a rise in its do wnload resp onse
time, to a maxim um v alue of 45.65 seconds at 2088 seconds in to the exp eriment.
After the link is restored, the v alue drops bac k do wn to h undreds of milliseconds,
therefore remaining b elo w 1 second till the end of the sim ulation.
6.4.5 HTTP P erfo rmance
The a v erage amoun t of HTTP traffic that is sen t and receiv ed in eac h top ology , are
sho wn in Figure 6.4 a to 6.4 d, for the normal and link failure conditions resp ectiv ely .
The a v erage amoun t of HTTP traffic sen t (in b ytes/s) during normal op erations,
is higher in the OPT routing top ology than in the A UTO routing top ology , as can
b e seen in Figure 6.3 a. While the trend is virtually constan t with time in the OPT
routing top ology , in the AUTO routing topology , it virtually slo wly decreases with
time. Ho w ev er, and unlike what w e observ ed with FTP traffic in Section 6.4.4 , the
amoun t of HTTP traffic that w as resp ectiv ely sen t in the OPT and A UTO routing
top ologies, is the same amoun t that is also resp ectiv ely received, as sho wn in Figure
6.4 c.
In the presence of a link failure, the same b eha vior is noticed for the HTTP traffic
sen t, Figure 6.4 b as for the HTTP traffic receiv ed, Figure 6.4 d. The amoun t of
HTTP traffic in the A UTO routing top ology rises immediately after link failure and
remains high during the whole failure p erio d. Immediately after the link is restored,
it falls bac k to lo w er v alues. The amount of HTTP traffic in the OPT routing
top ology drops immediately after the link failure o ccurs and keeps dropping slo wly
un til the link is restored. Thereafter it rises bac k to its old lev el and remains high
un til the end of the sim ulation.
The fact that the amoun t of HTTP traffic sent in the OPT and A UTO routing
top ologies differs b y so m uc h, Figure 6.4 a, while the amoun t of FTP traffic sent sho ws
no suc h difference b etw een the t w o top ologies, Figure 6.3 a, only helps to reinforce
the notion that differen t applications and proto cols can b e impacted differen tly b y
c hanges that are made on the net work or to the traffic flo wing through it. Therefore,
careful planning, testing and cautious implemen tation are v ery necessary .
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Chapter 6 Flo w Optimization using Mixed-In teger Programming (MIP)
The a v erage HTTP ob ject resp onse time is sho wn in Figure 6.4 e for the A UTO
and OPT routing top ologies resp ectiv ely . W e notice that the a v erage resp onse time
in the OPT routing top ology remains constan tly lo w, b elo w 13 milliseconds (v ery
go o d) from the start till the end of the sim ulation.
Figure 6.4: HTTP p erformance
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6.4 Results and Analyses
In con trast, the a v erage resp onse time in the A UTO routing top ology , slo wly and
steadily increases righ t from the start, un til ab out 1080 seconds in to the sim ula-
tion. It b ecomes highly v olatile from then on, while still increasing in trend. The
maxim um a verage response time of 3.45 seconds is recorded at 2736 seconds into
the 3600-seconds sim ulation. This is more than 265 times higher than the highest
recorded v alue in the OPT routing top ology .
A link failure in the A UTO routing top ology , causes a drop in HTTP av erage re-
sp onse time from a p eak of 2.23 seconds to a trough of 131 milliseconds, as can b e
seen in Figure 6.4 f. After the link is restored, the a v erage resp onse time increases
again, reac hing a p eak of 2.43 seconds (the maxim um in this scenario), b efore drop-
ping to v alues at/b elo w 1 second, to w ards the end of the sim ulation. W e mak e a
remarkable observ ation at this p oin t, based on the recorded resp onse time, whic h
drops substan tially immediately after the link failure o ccurs. After the link is re-
stored, it do es not only con tin ue to rise, but later drops again to relativ ely lo w er
v alues, as from the 2722 seconds time-p oin t un til the end of the sim ulation. The
maxim um resp onse time recorded in this scenario is far b elo w that recorded when
there w as no link failure.
In the OPT routing top ology , the same link failure causes a minor increase in the
a v erage HTTP ob ject resp onse time. This slo wly rises with the duration of the
failure, from 13 milliseconds to 222 milliseconds at the time of restoration. Ho wev er,
immediately after the failed link is restored, the v alues drop bac k to their normal
lev els at/b elow 13 milliseconds.
6.4.6 V oice P erfo rmance
All the applications and proto cols w e’ve analyzed so far in this c hapter are TCP-
based. In this and the next sub-section, w e are going to analyze the impact on
UDP-based applications, i.e. impact on v oice and video resp ectively .
W e next analyze the amoun t of v oice traffic sen t when using A UTO and OPT rout-
ing, resp ectively . Figure 6.5 a sho ws that sligh tly more v oice traffic is sen t in the OPT
routing top ology than in the A UTO routing top ology . Figure 6.5 b sho ws that there
are no adv erse effects on the v oice traffic that is sent in both top ologies, resulting
from the link failure in eac h of the top ologies.
Figure 6.5 c sho ws the v oice traffic that is receiv ed. It rev eals that m uc h less traffic
is receiv ed in the A UTO routing top ology than w as originally sen t. It also rev eals
that the amoun t of traffic receiv ed in the OPT top ology , is the same as the amount
that w as originally sen t. The effect of link failure on the amoun t of traffic that is
receiv ed, is sho wn in Figure 6.5 d. W e can deduct from it that the amoun t of v oice
traffic that is receiv ed in the OPT routing top ology is not affected b y the failure.
Ho w ever, in the A UTO routing top ology , we observ e a sligh t increase in the receiv ed
v oice traffic during the failure p erio d.
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Chapter 6 Flo w Optimization using Mixed-In teger Programming (MIP)
Figure 6.5: V oice sen t and received traffic
V oice P ack et Dela y
The end-to-end dela y of a v oice pac k et, also known as "analog-to-analog" or "mouth-
to-ear" dela y , is the sum of the individual delays of man y differen t comp onen ts, the
total of whic h is giv en by the follo wing form ula:
D V oice = D net + D enc + D dec + D compr + D decompr
where:
• D net denotes the network delay , i.e. the time in terv al b et ween when the sender
no de ga ve the pac k et to R TP to the time the receiv er got it from R TP .
• D enc denotes the enc o ding delay (on the sender no de) and is computed from
the enco der sc heme.
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6.4 Results and Analyses
• D dec denotes the de c o ding delay (on the receiver node), whic h is assumed to
b e equal to the enco ding dela y .
• D compr and D decompr denote the c ompr ession and de c ompr ession delays re-
sp ectiv ely and come f rom their corresp onding attributes in the V oice applica-
tion configuration of the sim ulator.
Th ab o ve v alues are automatically calculated b y the sim ulator. Statistics from all
activ e v oice no des in the net work are collected and the a v erage is calculated b y
the sim ulator. The In ternational T elecomm unication Union (ITU) G.114 standard
recommends a maxim um one-w ay dela y of 150 milliseconds for v oice pac k et trans-
missions [ 115 ].
The v oice pac ket end-to-end dela y under normal op eration is sho wn in Figure 6.6 a.
W e notice that, while the av erage dela y in the OPT top ology remains relativ ely
constan t from start to finish, that in the AUTO routing topology rises from the start
and b ecomes quite dynamic with time. Its maxim um v alue of 486.5 milliseconds is
recorded at 3456 seconds of sim ulation time, while that in the OPT routing top ology
remains b elo w 66 milliseconds.
Figure 6.6 b sho ws the effect of link failure on v oice pac k et end-to-end dela y . While
the v alue drops in the A UTO routing top ology , as a result of the link failure, w e
observ e a steady increase in the OPT routing top ology . Immediately after the link
is restored, the a v erage dela y v alue in the A UTO routing top ology increases again,
while that in the OPT routing top ology drops bac k to the lev el it had b efore the
failure.
V oice P ack et Dela y V a riation
The v oice pac k et dela y v ariation is simply the difference in the end-to-end dela ys of
selected v oice pac ket pairs within the same stream [ 46 ]. Remem b er that the end-to-
end dela y for a v oice pac k et is measured from the time it is created to the time it is
receiv ed.
Figure 6.6 c represen ts the measured v oice pac k et dela y v ariation under normal con-
dition. It sho ws a constan tly lo w v oice pac k et dela y v ariation (in picoseconds order
of magnitude), when using OPT routing. In con trast, a relativ ely higher and v ariable
v oice pac ket del a y v ariation is recorded when using A UTO routing. A maxim um
v alue of 81 milliseconds is attained at 2880 seconds in to the simulation.
As can b e seen from Figure 6.6 d, the single link failure causes no visible c hange to
the v olatile pattern already observ ed in the AUTO routing topology , in the absence
of failure (see Figure 6.6 c). Ho w ev er, a noticeable c hange is seen in the OPT routing
top ology . Its voice pac k et dela y v ariation increases from picoseconds (virtually zero)
to a maxim um of 21.4 milliseconds, as a result of the link failure. This how ev er drops
bac k to its pre-failure picoseconds-lev el immediately after restoration and remains
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Chapter 6 Flo w Optimization using Mixed-In teger Programming (MIP)
consisten tly at that lev el until the end of the sim ulation. Long after restoration in
the A UTO routing top ology , i.e. after 3456 of sim ulation time, the dela y v ariation
p eaks to a v alue of 84.6 milliseconds, b efore dropping bac k again to av eragely lo w er
v alues.
Figure 6.6: V oice p erformance
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6.4 Results and Analyses
V oice Jitter
A more elab orate definition and description of v oice jitter has already b een pro vided
in Chapter 5, Section 5.5.4 . In this subsection, we will just presen t its v alues as
measured in the A UTO and OPT top ologies resp ectiv ely .
Figure 6.6 e sho ws the measured jitters in the A UTO and OPT routing top ologies
under normal conditions. The jitter in the OPT routing top ology remains close to
zero and flat across the full duration of the sim ulation, while that in the A UTO
routing top ology v aries b et w een p ositiv e and negativ e p eaks at m ultiple time-p oin ts
during the sim ulation.
In the OPT routing top ology , the single link failure causes only a single minor
c hange in jitter. In the A UTO routing top ology , the same adds additional p ositiv e
and negativ e p eaks, as can b e seen in Figure 6.6 f.
Based on the measured jitter v alues and for the sak e of comparison, w e can conclude
that the OPT routing in 20-Links top ology offers b etter v oice p erformance than
A UTO routing in the same top ology . W e ho w ev er also lik e to men tion here that,
despite this difference in jitter v alues, none of the measured jitters in either the
A UTO routing top ology or the OPT routing top ology , comes ev en close to the
accepted tolerance of 30 milliseconds.
6.4.7 Video P erfo rmance
Video streaming is the other UDP-based application that w e use for p erformance
analysis and comparison in this study . In this sub-section, w e shall analyze its
p erformance in the 20-Link top ology , when A UTO and OPT routing are resp ectiv ely
used.
Sent and Received Video P ack ets
Figure 6.7 a sho ws the amoun t of video traffic that is sen t during normal op erations.
Figure 6.7 b sho ws the moun t that is sen t when the link with the highest throughput
fails. As can b e seen from b oth graphs, the same amoun t of video traffic is resp ec-
tiv ely sen t in the AUTO and OPT routing topologies, during normal op erations, as
w ell as in the presence of the single link failure. Not ev en the link failure seem to
affect the amoun t of video traffic that is sen t.
Lo oking at Figure 6.7 c, i.e. the amoun t of video traffic that is receiv ed during normal
op erations, w e notice a stark difference b etw een v alues in the OPT routing top ology
and those in the A UTO routing top ology . While all video traffic sen t in the OPT
routing top ology are also receiv ed at their final destinations, in the A UTO routing
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Chapter 6 Flo w Optimization using Mixed-In teger Programming (MIP)
top ology and after 555 seconds in to the sim ulation, only a fraction of the sen t traffic
is receiv ed.
Ev en when the link with the highest throughput fails, Figure 6.7 d sho ws that the
amoun t of receiv ed video traffic in the OPT routing top ology remains unaffected b y
the failure. In con trast, w e notice a clear increase in the amoun t of video traffic that
is receiv ed in the A UTO routing top ology , during the failure p erio d. A clear drop
in the amoun t of video traffic receiv ed is also obs erv ed, immediately after the link
is restored.
Figure 6.7: Video sen t and receiv ed traffic
Video P ack et Dela y
Figure 6.8 a sho ws the a verage video pac k et end-to-end dela y during normal op er-
ation. The pac k et end-to-end dela y in the OPT routing top ology is sho wn to sta y
b elo w 6 milliseconds from start to finish. In con trast to that, the a v erage video
pac k et end-to-end dela y in the A UTO routing top ology , is sho wn to increase steeply
to a p eak of 153 milliseconds within the first 612 seconds of the sim ulation. The
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6.4 Results and Analyses
maxim um v alue of 165.6 milliseconds is ho w ev er recorded a few seconds later at the
720 seconds sim ulation time-p oin t. A down w ard trend is then observ ed from thence
un til the end of the sim ulation, how ev er, with momen tary p eaks in-b et ween.
When the single link failure o ccurs, Figure 6.8 b sho ws that the pac k et end-to-end
dela y in the A UTO routing top ology , first decreases, then increases shortly b efore
dropping again. Ho w ev er, immediately after the failed link is restored, it immedi-
ately rises again. In the OPT routing top ology , the link failure causes the end-to-end
dela y to steadily increase during the full duration of the failure. Immediately after
the link is restored, the dela y drops bac k to the same low lev els it had b efore the
failure.
Figure 6.8: Video p erformance
Video P ack et Dela y V a riation
The video pac k et delay v ariation under normal op erating condition is sho wn in
Figure 6.8 c. W e observ e a constan tly increasing pac k et dela y v ariation in the A UTO
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Chapter 6 Flo w Optimization using Mixed-In teger Programming (MIP)
routing top ology , while the same remains steadily low and close to zero, in the OPT
routing top ology .
Although the same rising pattern is also observ ed when link failure o ccurs in the
A UTO routing top ology , Figure 6.8 d sho ws that the graph flattens a bit during
the full p erio d of the failure. It then rises again like before, after the failed link is
restored. This means the link failure causes a sligh t reduction in the pac k et dela y
v ariation in the A UTO routing top ology .
On the other hand, w e notice that the pac k et dela y v ariation in the OPT routing
top ology remains lo w as long as there are no failures. Immediately after the link fails,
the v alue of the dela y v ariation rises sharply , un til the link is restored. Immediately
after the restoration, it starts dropping, but not bac k to old lev els. W e instead
observ e a m uc h slo w er decrease with time. As Figure 6.8 d shows, despite the steady
drop from then un til the end of the sim ulation, the v alue still remains relativ ely
higher than what it w as b efore the failure o ccurred.
6.5 Summa ry
T o impro ve the performance in a congested backbon e net w ork, w e first form ulate
and solv e a Mixed-In teger Programming (MIP) problem based on the same top ology .
The solution pro vides optimized OSPF in terface costs, whic h w e man ually transfer
to our main sim ulator. W e next design t wo kinds of topology , based on the same
20-Links top ology , but using t w o v ariants of OSPF routing. In one (the A UTO
routing top ology), OSPF uses the default (automatic) calculated routing metrics.
In the other (the OPT routing top ology), it uses the MIP-determined and manually
added optimized metrics. W e run t w o sets of sim ulations for eac h routing v arian t,
one without link failure and the other with a 10-min ute single link failure in the
middle of the sim ulation.
The obtained results sho w impro v ed p erformance of man y magnitudes in scenarios
based on OPT routing. Ho w ev er, in some failure instances, w e notice b etter p er-
formance in the A UTO routing top ology against that in the OPT routing top ology .
The reasons for this are pro vided in the subsequen t paragraphs.
Normally , when a link fails, it triggers the recalculation and selection of alternativ e
paths that do not include the failed link. Ho w ev er, if the failed link b elongs to a
set of equal-cost paths, OSPF simply con tin ues to push traffic via the remaining
accessible paths, without generating new routing up dates.
First reason: W e notice that the A UTO routing top ology has no equal-cost paths,
while the OPT routing top ology has m ultiple equal-cost paths for m ultiple Origin-
Destination (OD) pairs. F urther analysis rev eals that the failed link b elongs to one
of suc h. Therefore, while OSPF is able to re-calculate new costs and determine new
paths for traffic flo ws in the A UTO routing top ology that are affected b y the link
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6.5 Summary
failure, in the OPT routing top ology , it simply reroutes the affected flo ws via the
remaining paths in the equal-cost set.
Second reason: In the OPT routing top ology , equal-cost do es not necessarily mean
equal bandwidth/capacit y . Therefore, in the sp ecial case where link failure forces
traffic redistribution via paths with lo w er capacity , these migh t immediately suffer
under the additional load, if more capacit y is required than is av ailable on the links
(or paths).
The second reason ab o ve also explains wh y T able 6.2 b that records utilization and
throughput in the OPT routing top ology during failure, has a few “red” (o v er-
utilized) links, but T able 6.1 b that records the same under normal (no failure)
condition, has none.
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7
Conclusion
This chapter summarizes our findings, discusses lessons le arne d and outlines futur e
r ese ar ch dir e ctions.
In ternet traffic is exp ected to keep gro wing, as more users, devices, services and
applications are b eing added. With the pac k et forw arding In ternet also taking o v er
more fundamen tal roles, e.g. from legacy infrastructures suc h as circuit-switc hed
telecomm unication and radio/TV broadcast comm unications, its traffic comp osition
is also exp ected to increase and exhibit m uc h broader div ersit y .
A detailed understanding of mo dern In ternet traffic comp osition and b eha vioral pat-
terns is necessary to prop erly address their asso ciated management and performance
c hallenges.
7.1 Managing P2P T raffic
Con ten t-sharing P2P netw orks build logical (application-lev el) top ologies on top of
the In ternet’s routing underla y top ology . The large prop ortion of bac kb one capacit y
they consume, the disruptiv e nature of their traffic and the h uge c hallenges these
all p ose to ISPs, pro vok ed h uge in terest in the researc h comm unity , leading to m ul-
tiple researc h studies and b etter understanding of P2P systems and the issues they
create.
In this thesis, w e prop ose a no v el, practical and simple approac h that, on one hand,
helps ISPs address the traffic managemen t and p erformance c hallenges attributed
to P2P traffic and on the other hand, b o osts general P2P user exp erience. This
win-win solution is attained via ISP and P2P collab oration, enabled b y our newly
prop osed service, whic h w e call, the Or acle service.
T o assess our prop os itions and quan tify the gains made p ossible by using the Oracle
service, w e carry out a series of pac k et-lev el sim ulations on un biased and oracle-
biased top ologies. W e then analyze and compare their p erformance using selected
metrics. Multiple b enefits are observ ed in Oracle-biased top ologies.
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Chapter 7 Conclusion
First, w e analyze the graph structural prop erties of the un biased and biased o v erla y
net w orks. W e use a visualization to ol to show the stark structural con trast that
exist b et ween the un biased and biased P2P o v erla ys. While the un biased ov erla y
is sho wn to ha ve no particular structure, the biased o v erla y is sho wn to ha v e a
structure that is more aligned with the ISP’s underla y top ology , since the Oracle
service facilitates the construction of a more meaningful (lo calized) o v erla y . This
mitigates the o v erlay/underla y mismatc h, whic h in un biased o v erla ys, is caused b y
the random and selfish connection approac h that p eers use. Although biased o verla ys
sho w a sligh t increase in graph diameter and small decrease in mean no de degree,
these are sho wn to ha ve no adv erse effects on the net w ork p erformance as a whole.
W e next analyze the distribution of queries and resp onses (hits) in the o v erla ys,
including the in ter-AS traffic, then compare the measured a v erage do wnload times
of p eers in the un biased and Oracle-biased top ologies. W e observ e unhamp ered
distribution of queries and resp onses in the Oracle-biased o v erla y , just lik e they are
in the un biased o ver la y . How ev er, in the case of the biased o verla y , most of these are
lo calized within an AS. The ISP th us retains a h uge prop ortion of the P2P traffic
within its o wn domain and also reduces the prop ortion of in ter-AS traffic b y large
factors. These result in huge cost sa vings for the ISP and faster con ten t do wnload
times for the p eers.
T o ev aluate the p erformance of the Oracle service under differen t user b eha vioral
conditions, w e carry out additional exp eriments using mathematical distributions
that represen t b est, normal and w orst conditions. In all but a few cases, the results
sho w that p erformance in the Oracle-biased ov erla y are m uc h b etter than in the
un biased o v erla y .
Based on the presen ted results, w e argue that offering the Oracle service brings lots of
b enefits to the ISP . These include increase con trol o v er P2P traffic, impro v ed traffic
engineering abilit y , b etter service to customers and h uge cost savings on transit fees.
By using the Oracle service, P2P users also con tribute to the increased lo calit y and
reap h uge b enefits from it as w ell. They no longer need to infer net w ork conditions
themselv es. Increased lo calit y of query hits translates in to b etter do wnload times
for the p eers.
In general, reduced o verla y/underla y mismatc h, increased P2P lo calit y and reduction
in o v erhead traffic, amount to a more efficien t and scalable net w ork.
7.2 T op ologies and T raffic Flo ws
T o study ho w the top ology of a bac kb one net w ork affects traffic flow and general
p erformance, w e mo del 3 distinct top ologies that ha v e the same n um b er of no des
(i.e. 12), but differ in the n um b er of their links, structural c haracteristics and total
bandwidth capacit y . The 3 top ologies consists of i) a F ullmesh top ology , with 66
138

7.2 T op ologies and T raffic Flo ws
links and the highest total bandwidth capacit y of appro ximately 285 Gbps, ii) a 30-
Links top ology with a mo derate total capacit y of appro ximately 195 Gbps and iii)
a 20-Links top ology , with the least total capacit y of appro ximately 132.5 Gbps.
W e p erform a series of simulation studies, applying the same traffic load and net w ork
conditions on eac h of the 3 top ologies, then analyze and compare their effects on
p erformance using selected p erformance metrics.
F or eac h top ology , w e analyze the p erformance under normal (baseline) traffic load
and when the traffic load increases b y 35%. W e also analyze the effect of a single
link failure under baseline traffic, as w ell as when the traffic load increases by 35%.
F or the single link failure analysis, w e select the link in each topology that has the
highest throughput at baseline traffic.
In nearly all analyzed and compared categories, the b est p erformances are observ ed
in the F ullmesh top ology , whic h also sho ws the b est tolerance and resilience against
the single link failure, even at increased traffic load. How ev er, for large backbone
net w orks with many nodes, full-mesh top ologies are impractical b ecause they lac k
scalabilit y and are v ery exp ensiv e to build and op erate. Despite the high path
div ersit y in the F ullmesh top ology , w e observ e that only a few of these paths are
used. This can b e attributed to the default b est path route-selection mec hanism of
the routing proto col that selects only the b est path (based on metrics) out of man y
p oten tial paths to include in its forw arding table. As a result, man y links in the
top ology are not used at all.
On the con trary , the 20-Links top ology , with less than one-third the n um b er of
links and a little less than half the total capacit y of the F ullmesh top ology , sho ws
comparable p erformance only a in a v ery limited num b er of cases, during baseline
traffic load. In most cases, the p erformance in the 20-Links top ology comes last.
The same top ology is also observ ed to ha v e the least tolerance and resiliency against
link failure.
In a ma jorit y of the cases, the 30-Links top ology , whic h has less than half the
n um b er of links, but more than t wo-thirds the capacit y of the F ullmesh top ology , is
observ ed to offer the same lev el of p erformance as the F ullmesh top ology . Ho w ev er,
it also sho ws less tolerance and resilience against link failure, compared to those
offered in the F ullmesh top ology . Nev ertheless, one can still argue that, b ecause of
its enormously reduced n um b er of links and comparable p erformance to that of the
F ullmesh top ology , it offers the b est cost/p erformance/tolerance trade-off of all 3
top ologies, while the 20-Links top ology offers the least trade-off.
As a reference net w ork top ology used b y man y other researc h groups, the reference
bac kb one netw ork top ology for German y offers us the abilit y to obtain results that
are generally v erifiable and comparable. W e use the 12 no de mo del to reflect recen t
up dates to the original mo del. The reduced n umber of no des in the new reference
top ology also has additional adv antages, suc h as reduced CPU pro cessing and shorter
sim ulation runs. W e still do think that larger top ologies consisting of man y more
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Chapter 7 Conclusion
no des w ould generally offer similar results if these share the same kind of structural
prop erties (e.g. a verage node degree) and bandwidth distribution lik e the top ologies
used in our studies.
7.3 Optimizing T raffic Flo ws
On further assessmen t of the 20-Links top ology under increased traffic load, an
imp ortan t observ ation is made. A few ma jor links are noticed to b e suffering from
congestion, while others (of lesser bandwidth) ha v e little or no traffic on them. Could
this situation b e impro v ed if the flo w is optimized to a v oid congestion on suc h links?
W e think and sho w that it could b e impro ved.
W e prop ose an optimization metho d that exploits the use of MIP to minimize the
maxim um utilization on eac h link and efficien tly distribute the traffic load in the
top ology . This approac h offers many benefits to the ISP , since it do es not in v olv e
the addition of new links/no des or capacit y upgrades on exiting links and can b e
accomplished within min utes. It is of ma jor imp ortance that th e general p erformance
m ust not suffer, as a result of the prop osed approac h. P erformance analysis therefore
constitutes a ma jor part of the analyses to assess the practicabilit y and usability of
this approac h.
MIP is used to determine the b est in terface cost for optimized OSPF routing within
the top ology . The p erformance in the optimized routing top ology (OPT) and that
in the automatic (default) routing top ology (A UTO) are analyzed and compared.
The results sho w significan t differences in p erformance b etw een the t w o compared
top ologies. The OPT routing top ology is ob serv ed to offer b etter p erformances than
the A UTO routing top ology in nearly all compared categories. Despite the b etter
p erformance in the absence of link failure, the OPT top ology sho ws less resilience
to failure than the A UTO top ology . The is ho w ev er not a general issue, but one
constrain t to our selected top ology . The reasons for this are giv en in Chapter 6
Section 6.5 . The issue can easily b e resolv ed or b e prev en ted through careful planning
and analysis.
The cost-sa vings with this approac h are enormous. Instead of immediately upgrad-
ing those links in the top ology that app ear to b e congested, an ISP can use our
prop osed cost-effectiv e, fast and straigh t-forw ard approac h to engineer the traffic
flo w and so, p ostp one exp ensiv e upgrades b y a few cycles in to the future.
Generally , underutilized or sufficien tly o v er-pro visioned net w orks b enefit little from
(and do not need) suc h optimization, since the routing proto col (e.g. OSPF and
IS-IS, via use of the SPF algorithm) already ensures that the b est route is selected
for pac k et forwarding. The F ullmesh and 30-Links top ologies fall in this category .
Ho w ever, in net w orks where the capacit y on some paths start to run out and the
net w ork still p ossesses enough capacit y on other paths, optimization b ecomes a
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7.4 Outlo ok
viable and less exp ensiv e option. The 20-Links top ology (at 35% increased traffic)
falls in this category .
7.4 Outlo ok
W e ha ve sho wn in this thesis ho w the ISP can use collab oration to address funda-
men tal traffic managemen t challenges on its IP bac kb one net w ork. W e hav e also
sho wn ho w in the absence of suc h collab oration, the ISP can still use other already
a v ailable resources to con trol and manage traffic flo ws. The c hallenges p osed b y
P2P can also b e p osed b y an y other future application or trend. Our w ork on ISP
and P2P collab oration offers a foundational approac h and example for similar cases
of join t in terest, in v olving ev en comp eting parties. It sho ws ho w collab oration fa-
cilitates mitigation of suc h issues and offers b enefits to all parties. The In ternet
comm unit y recognizes the need for suc h and is curren tly w orking on standardized
proto cols based on com bined contributions from our w ork and those from a few
other groups.
The ISP’s readiness to deal with highly dynamic conditions and fast-paced activities
on the In ternet, demands for innov ativ e approac hes that do not only offer solutions,
but for approac hes that offer quic k and cost-effective solutions. With regards to
traffic managemen t, forecasts and trends (e.g. Artificial In telligence (AI), In ternet
of Things (IoT), big data and cloud services) indicate increasingly that more complex
c hallenges a wait the ISP in the near future.
Irresp ectiv e of all past and current traffic managemen t and flo w optimization ap-
proac hes, the one thing that most researc hers and op erators agree on, is that a
radical c hange of the net w ork’s curren t arc hitecture is necessary to effectiv ely deal
with future traffic comp ositions/v olumes/flo ws and their managemen t c hallenges
[ 12 , 157 , 174 ]. The go o d news is that this c hange has already b egun. With the
recen t shift to wards Soft w are Defined Net w orking (SDN) [ 11 , 116 , 127 , 140 , 153 ],
the virtualization of the net w ork [ 29 , 30 , 48 , 222 ] and the separation of the control,
data and managemen t planes in to separate en tities, the net w ork infrastructure is
b ecoming more simplified. It can no w b enefit from b etter programmability , scala-
bilit y and flexibilit y . This is giving rise to new er arc hitectures, suc h as the segment
routing arc hitecture [ 31 , 58 , 60 ], whic h is m uc h more scalable, flexible, less complex
and easier to manage/op erate than IP routing or MPLS. F urther, with easier ac-
cessibilit y to the most up-to-date information on net w ork conditions, con trollers in
SDNs are no w able to react m uch faster to c hanges and failures. This enormously
facilitates traffic engineering in SDNs, far b ey ond the lev els p ossible in curren t and
past net w orks.
Appropriate measuremen t approac hes will alwa ys accompan y whatev er arc hitectural
c hanges are made, in order for ISPs to b e able to assess and fully understand the
impact of these com bined effects on their net work. F or example, the curren t big
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Chapter 7 Conclusion
data rev olution demands for appropriate measuremen t and handling to ols b y their
resp ectiv e stakeholders. This includes ISPs, whose net w orks pro vide the medium
through whic h big data flo ws from the differen t sources where they are generated to
the resp ectiv e lo cations where they are collected/analyzed.
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