Cosc heduling in the Multicore Era
The Ar t of Doing Things Simul t aneousl y
v orgelegt v on
Dipl.-Inform.
Jan H. Sc hönherr
v on der F akultät IV – Elektrotec hnik und Informatik
der T ec hnisc hen Univ ersität Berlin
zur Erlangung des akademisc hen Grades
Doktor der Ingenieurwissensc haften
– Dr.-Ing. –
genehmigte Dissertation
Promotionsaussc h uss:
V orsitzender: Prof. Dr. Odej Kao
Gutac h ter: Prof. Dr. Hans-Ulric h Heiß
Gutac h ter: Prof. Dr.-Ing. Jan Ric hling
Gutac h ter: Prof. Dr. Andreas P olze
T ag der wissensc haftlic hen A ussprac he: 19. Juni 2019
Berlin 2019
ii
Kurzzusammenfassung
Der Begriff Cosche duling b ezeic hnet in seiner w eitesten Definition die b ewusst
gleic hzeitige A usführung ausgew ählter T asks auf mehreren CPUs. Cosc heduling
und der v erw andte Begriff Gang Sche duling wurden in den 80er bzw. 90er Jahren
geprägt, als die ersten Mehrprozessorsysteme en t wic k elt wurden, bzw. sie w eitere
V erbreitung fanden.
Die garan tierte gleic hzeitige A usführung v on b estimm ten T asks erlaubt die
effizien te Realisierung v on feingran ularer Sync hronisation und K omm unikation,
ohne dass es zu möglic herw eise langen W artezeiten k omm t, w eil auf einen gera-
de nic h t laufenden T ask gew artet w erden m uss. Im V ergleic h zu b eispielsw eise
Stap elv erarb eitung o der der P artitionierung des Systems im Raum erlaubt es
Cosc heduling, die gerade laufende parallele An w endung zu Gunsten einer ande-
ren zu un terbrec hen, w o durc h Sc hedulingv erfahren n un nic h t mehr auf einzelnen
sequen tiellen T asks op erieren, sondern auf ganzen parallelen An w endungen.
Seitdem hat sic h die IT-Landsc haft deutlic h w eiteren t wic k elt: aufgrund des
Siegeszugs des (nic h t parallelen) PCs ist die Thematik P arallelität zunäc hst in
den Hin tergrund getreten. Erst als w eitere T aktsteigerungen nic h t mehr mög-
lic h w aren, wurde P arallelität wieder in teressan t. Im V ergleic h zu früher gibt es
allerdings zw ei b edeutende Un tersc hiede:
1. Durc h die v ergleic hsw eise langsame Wiedereinführung v on parallelen Sys-
temen fand k eine Neuen t wic klung v on Soft w are statt, sondern eine A d-
aption. Damit w erden aktuelle Mehrk ernsysteme eher als v erteiltes Sys-
tem denn als paralleles System b ehandelt: aktuelle Betriebssysteme k ennen
das K onzept einer parallelen An w endung nic h t; Cosc heduling und andere
Managemen tansätze sind ihnen fremd, w orun ter die A usführung tatsäc h-
lic h paralleler An w endungen leidet. Zudem wird herangehenden Soft w are-
en t wic klern (außerhalb der HPC-Nisc he) die En t wic klung en tsprec hender
Soft w are selten nahe gebrach t.
2. Aktuelle Mehrk ernsysteme un tersc heiden sic h in ihrer Arc hitektur stark
v on frühen parallelen Systemen und Clustern. Dies führt dazu, dass sic h
die Lösungen v on damals nic h t ohne w eiteres auf heutige Systeme üb ertra-
gen lassen. In b esondere gibt es in heutigen Systemen div erse Ressourcen,
die sic h mehrere CPUs teilen m üssen, wie z. B. Sp eic herbandbreite o der
Cac hes. Dies führt zu Ressourcenengpässen, die zu mehr o der w eniger star-
k en Leistungsein bußen individueller T asks führen k önnen. Die F orsch ung
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iv
sc hlägt hier zumeist ein gesc hicktes Gruppieren v on T asks v or, so dass
Ressourcenengpässe nac h Möglic hk eit minimiert w erden – im Prinzip eine
andere F orm v on Cosc heduling. W ährend jeder V orsc hlag für sic h seine
Daseinsb erec h tigung hat, ist eine In tegration in b estehende Systeme meist
unpraktikab el, da der F okus oft eingesc hränkt ist und die v ersc hiedenen
Ideen in K onflikt miteinander stehen.
In dieser Dissertation wird das K onzept Cosche duling auf heutige Mehrk ern-
systeme üb ertragen und adaptiert. Die früher gültigen V or- und Nac h teile w er-
den neu b ew ertet und alte wie neue An w endungsszenarien b etrac h tet. Aus einem
Anforderungskatalog heraus wird ein Cosc hedulingv erfahren k onzipiert, das den
heutigen Erw artungen v on An w endungs- und Betriebssystemen t wic klern gerec h t
wird. Es wird eine Metho de dargelegt, wie b esagtes V erfahren in b estehende
Betriebssysteme in tegriert w erden kann, ohne dass es zu einer wesen tlic hen V er-
haltensänderung des ursprünglic hen Sc hedulers komm t. Es wird gezeigt, w elc he
neuen Managemen tmöglic hk eiten sic h dadurc h für das Betriebssystem ergeb en
und w elc hen Einfluss dies in Zukunft auf die En t wic klung paralleler Anw endun-
gen und Betriebssysteme hab en wird.
Abstract
In its most general definition, c osche duling refers to a delib erate sim ultaneous
execution of certain tasks on m ultiple CPUs. The concepts of cosc heduling
and gang sc heduling ha v e b een in tro duced in the early eigh ties and nineties,
resp ectiv ely . A t that time, the first massiv ely parallel systems w ere dev elop ed
and b ecame more widely used.
The guaran tee of sim ultaneous execution of certain tasks allows to mak e
use of fine-grained sync hronization efficien tly: it is imp ossible for a curren tly
executing task to w ait for another task that do es not mak e progress. Compared
to batc h pro cessing and partitioning in space, coscheduling allo ws to preempt
a running parallel application in fa v or of another more imp ortan t application.
This con text switc h at application lev el offers more flexibilit y for sc hedulers.
Since then, there ha v e b een thorough c hanges in the computing landscap e:
(non-parallel) p ersonal computers ha v e p enetrated the mark et, and with it, par-
allelism had b ecome a second-class citizen. P arallelism has only recen tly b ecome
imp ortan t again, after single-thread p erformance could not b e increased b y the
usual margins an ymore. Ho w ev er, parallelism to da y differs from parallelism bac k
then, mostly b ecause of the follo wing t w o reasons:
1. The rein tro duction of parallel systems happ ened not o v ernigh t but as a
gradual pro cess. Due to this, existing soft w are w as adapted to run on
parallel systems instead of b eing rewritten. This adapted soft w are han-
dles con temp orary m ulticore systems as if they w ere distributed systems
instead of the parallel systems they are: curren t op erating systems ha v e
no concept of a parallel application; they do not kno w ab out cosc heduling
or other managemen t approac hes, hamp ering real parallel applications.
T o mak e matter w orse, budding soft w are dev elop ers (outside of the HPC
nic he) are seldom taugh t the subtleties of parallel soft w are dev elopmen t.
2. The prop erties of to day’s m ulticore arc hitectures differ substan tially from
early parallel systems and clusters. F or this reason it is not alw a ys p ossible
to reuse once v alid solutions. In particular, CPUs in to da y’s system ha v e
to share a m ultitude of resource, such as memory bandwidth of cac hes.
This results in resource con ten tion with a more or less noticeable impact
on p erformance of individual tasks. In most cases researc h suggests to
a v oid or reduce resource con ten tions b y grouping tasks skillfully – whic h is
basically a form of cosc heduling. Ho w ev er, an in tegration of these researc h
v
vi
ideas in to existing systems is impractical more often than not, b ecause
individual ideas – while solving their sp ecific use case – are difficult to
com bine with their rather narro w fo cus.
This thesis is p orts the concept c osche duling to con temp orary m ulticore arc hi-
tectures. Adv an tages and disadv an tages kno wn from other parallel arc hitectures
are reev aluated, and a catalog of use cases – old and new – is compiled. The
com bined requiremen ts of these use cases form the basis for a v ersatile cosc hedul-
ing mo del. A flexible cosc heduling approac h is devised, whic h measures up to
to da y’s exp ectations of application and op erating system dev elop ers alik e. In
particular, a metho d is included that allo ws to in tegrate cosc heduling functional-
it y in to existing general purp ose sc hedulers without c hanging their c haracteristic
traits substan tially . Newly enabled management opp ortunities within the op er-
ating system are discussed and their influence on the design of up coming parallel
applications and op erating systems are explored.
T o Cindy.
Without her I would not have emb arke d on this journey.
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viii
Con ten ts
Kurzzusammenfassung iii
Abstract v
Con ten ts ix
1 In tro duction 1
1.1 The Significance of Coscheduling . . . . . . . . . . . . . . . . . . 2
1 . 2 P r o b l e m S t a t e m e n t .......................... 3
1 . 3 C o n t r i b u t i o n .............................. 4
1.4 Structure of this Thesis . . . . . . . . . . . . . . . . . . . . . . . . 4
1 . 5 C h a p t e r N o t e s ............................. 5
I Cosc heduling F oundation 7
2 Wh y Cosc heduling? 11
2.1 Application Design . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 . 1 . 1 A c t i v e W a i t i n g ........................ 1 3
2.1.2 Sync hronous Execution . . . . . . . . . . . . . . . . . . . . 18
2.1.3 Arc hitecture-sp ecific Optimizations . . . . . . . . . . . . . 21
2.2 Resource Managemen t . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.1 Impro v ed Data Sharing . . . . . . . . . . . . . . . . . . . . 23
2.2.2 Reduced Resource Conten tion . . . . . . . . . . . . . . . . 24
2.2.3 Ph ysical Op eration . . . . . . . . . . . . . . . . . . . . . . 26
2 . 3 S y s t e m D e s i g n ............................. 2 7
2.3.1 Application Management . . . . . . . . . . . . . . . . . . . 27
2 . 3 . 2 S e c u r i t y ............................ 3 0
2.3.3 Device Management . . . . . . . . . . . . . . . . . . . . . 31
2 . 4 C h a p t e r N o t e s ............................. 3 3
3 Cosc heduling Mo del 35
3 . 1 B a s i c T e r m s .............................. 3 5
3 . 1 . 1 S o f t w a r e ............................ 3 6
3 . 1 . 2 H a r d w a r e ........................... 3 7
3 . 1 . 3 P a r a l l e l i s m ........................... 3 8
ix
x CONTENTS
3.2 Cherry-pic king Cosc heduling T erms . . . . . . . . . . . . . . . . . 38
3.2.1 Ousterhout’s Coscheduling . . . . . . . . . . . . . . . . . . 38
3.2.2 F eitelson’s Gang Sc heduling . . . . . . . . . . . . . . . . . 39
3.2.3 Arpaci-Dusseau’s Implicit Coscheduling . . . . . . . . . . . 40
3.2.4 VMw are’s Relaxed Cosc heduling . . . . . . . . . . . . . . . 40
3 . 3 A s p e c t s o f T i m e ............................ 4 1
3 . 4 A s p e c t s o f S p a c e ........................... 4 4
3 . 5 C o s c h e d u l e d S e t s ........................... 4 6
3 . 6 T h e o r y v s . P r a c t i c e .......................... 5 0
3 . 7 C h a p t e r N o t e s ............................. 5 2
4 Cosc heduling Approac hes 53
4.1 Ousterhout’s Cosc heduling . . . . . . . . . . . . . . . . . . . . . . 53
4.2 Distributed Hierarc hical Con trol . . . . . . . . . . . . . . . . . . . 54
4.3 Distributed Queue T ree . . . . . . . . . . . . . . . . . . . . . . . . 56
4.4 Cosc heduling in VMw are ESXi . . . . . . . . . . . . . . . . . . . 56
4.5 F urther Considerations . . . . . . . . . . . . . . . . . . . . . . . . 57
4 . 6 C h a p t e r N o t e s ............................. 5 8
I I Cosc heduler Design 59
5 Design Rationales 63
5 . 1 V e r s a t i l i t y ............................... 6 3
5 . 2 S c a l a b i l i t y ............................... 6 4
5 . 3 I n t e r a c t i v i t y.............................. 6 4
5 . 4 N o n - i n t r u s i v e n e s s ........................... 6 5
5 . 5 C h a p t e r N o t e s ............................. 6 5
6 T op ology-a w are Cosc heduling 67
6 . 1 B a s i c S t r u c t u r e ............................ 6 7
6.1.1 Sync hronization Domains . . . . . . . . . . . . . . . . . . 67
6 . 1 . 2 S c h e d u l i n g ........................... 6 9
6 . 1 . 3 F a i r n e s s ............................ 7 0
6.2 Mapping of Coscheduled Sets . . . . . . . . . . . . . . . . . . . . 74
6.2.1 Time constraints . . . . . . . . . . . . . . . . . . . . . . . 75
6.2.2 Placemen t constrain ts . . . . . . . . . . . . . . . . . . . . 76
6 . 3 L o a d B a l a n c i n g ............................ 7 7
6.4 F ragmen tation a v oidance . . . . . . . . . . . . . . . . . . . . . . . 79
6 . 4 . 1 I d l e s e l e c t i o n ......................... 8 0
6.4.2 Asymmetric SD shap es . . . . . . . . . . . . . . . . . . . . 80
6.5 T rac king of CPU time . . . . . . . . . . . . . . . . . . . . . . . . 81
6 . 6 C h a p t e r N o t e s ............................. 8 4
CONTENTS xi
7 In tegrating T A CO 85
7.1 Requiremen ts for In tegration . . . . . . . . . . . . . . . . . . . . . 85
7.2 Con v ersion of Non-cosc heduling Sc hedulers . . . . . . . . . . . . . 86
7.3 Cosc heduling in Lin ux . . . . . . . . . . . . . . . . . . . . . . . . 89
7.3.1 Lin ux Sc heduler Basics . . . . . . . . . . . . . . . . . . . . 89
7.3.2 Sc heduled T ask Groups . . . . . . . . . . . . . . . . . . . . 93
7 . 3 . 3 L o a d B a l a n c i n g ........................ 9 5
7 . 3 . 4 C u r r e n t S t a t e ......................... 9 7
7.4 Cosc heduling in F reeBSD . . . . . . . . . . . . . . . . . . . . . . . 98
7.4.1 F reeBSD Sc heduler Basics . . . . . . . . . . . . . . . . . . 98
7 . 4 . 2 D e s i g n O u t l i n e ........................ 1 0 0
7.4.3 Comparison with Linux In tegration . . . . . . . . . . . . . 103
7 . 5 C h a p t e r N o t e s ............................. 1 0 4
8 Analysis and Ev aluation 105
8.1 Collectiv e Con text Switch . . . . . . . . . . . . . . . . . . . . . . 106
8 . 1 . 1 D i r e c t C o s t s .......................... 1 0 6
8.1.2 Indirect Costs . . . . . . . . . . . . . . . . . . . . . . . . . 108
8 . 2 F r a g m e n t a t i o n ............................. 1 0 9
8.2.1 In ternal F ragmen tation . . . . . . . . . . . . . . . . . . . . 109
8.2.2 External F ragmen tation . . . . . . . . . . . . . . . . . . . 110
8 . 3 P r e e m p t i o n .............................. 1 1 1
8 . 4 E x p e r i m e n t s .............................. 1 1 2
8.4.1 Cosc heduling of Virtual Mac hines . . . . . . . . . . . . . . 112
8.4.2 Cosc heduling of P arallel Programs . . . . . . . . . . . . . . 113
8.4.3 Un used Cosc heduling F unctionalit y . . . . . . . . . . . . . 116
8 . 5 C h a p t e r N o t e s ............................. 1 1 7
I I I A dv anced Cosc heduling 119
9 Managemen t of P arallel Applications 123
9.1 Managemen t Considerations . . . . . . . . . . . . . . . . . . . . . 124
9.2 Managemen t Strategy . . . . . . . . . . . . . . . . . . . . . . . . 125
9.2.1 Basic Equipartitioning with T A CO . . . . . . . . . . . . . 125
9 . 2 . 2 O p t i m i z a t i o n s ......................... 1 2 7
9 . 3 E v a l u a t i o n ............................... 1 2 9
9.3.1 W orkload and Ev aluation System . . . . . . . . . . . . . . 129
9.3.2 Considered Approaches . . . . . . . . . . . . . . . . . . . . 130
9 . 3 . 3 R e s u l t s ............................. 1 3 2
9.3.4 Exploring the Design Space . . . . . . . . . . . . . . . . . 136
9 . 4 R e l a t e d W o r k ............................. 1 3 8
9 . 5 C h a p t e r N o t e s ............................. 1 3 9
xii CONTENTS
10 Sc heduling with T urb o Bo ost 141
10.1 Issues with T urb o Bo ost . . . . . . . . . . . . . . . . . . . . . . . 142
10.2 T urb o Bo ost with Cosc heduling . . . . . . . . . . . . . . . . . . . 143
1 0 . 3E v a l u a t i o n ............................... 1 4 6
10.4 Discussion and Related W ork . . . . . . . . . . . . . . . . . . . . 149
1 0 . 5C h a p t e r N o t e s ............................. 1 5 0
11 System Design Opp ortunities 151
11.1 Resource Con ten tion and P arallel Programs . . . . . . . . . . . . 151
11.2 A daptiv e Lo c ks with Cosc heduling . . . . . . . . . . . . . . . . . 153
11.3 Concurrency Managemen t . . . . . . . . . . . . . . . . . . . . . . 155
11.4 Refined Affinit y Managemen t . . . . . . . . . . . . . . . . . . . . 156
1 1 . 5C h a p t e r N o t e s ............................. 1 5 8
12 Conclusion 159
Bibliograph y 163
Glossary 177
A cron yms 179
Chapter 1
In tro duction
The sc heduler of an op erating system is resp onsible for managing the CPUs of
a computer system, assigning them to the differen t tasks presen t in the system.
This generation of a sc hedule within a dynamic system is usually non trivial as dif-
feren t goals, suc h as p erformance or energy efficiency , collide. Ev en when a goal
is finally set, the problem remains NP-complete and migh t require kno wledge
that is una v ailable in general purp ose computer systems, suc h as future b eha v-
ior of tasks. Hence, scheduling usually relies on heuristics to op erate somewhere
near the in tended goal.
In m ulticore systems the resource CPU is not unique an ymore, complicating
the managemen t of this resource. T o da y , CPUs are handled as anon ymous re-
sources, allo cated and deallo cated to tasks b y the op erating system sc heduler on
ev ery con text switc h. In general, there is neither a direct con trol o v er this allo ca-
tion pro cess a v ailable for applications, nor some kind of notification mec hanism
when the op erating system mo difies the resource allo cation. Applications can
request CPUs only indirectly b y creating one or more tasks. (Though, most op-
erating systems allo w applications to bind tasks to sp ecific CPUs.) This lac k of
con trol within applications allo ws the op erating system a great deal of flexibilit y
to reac h its sc heduling goal, as there are (almost) no external constrain ts on task
placemen t. F or a wide range of applications, this lac k of con trol and guaran tees
do es not p ose a problem. One notable exception are real-time tasks, where to o
little CPU time leads to missed deadlines, whic h can ha v e disastrous results in
the ph ysical w orld. Another exception is the class of parallel applications, which
can suffer extreme p erformance losses due to non-parallel execution.
T o efficien tly supp ort all application classes, the control of the resource CPU
m ust b e made more explicit. Ho w ev er, more sophisticated approac hes to manage
CPUs are rarely found. And some more wide spread managemen t concepts,
suc h as explicit con trol o v er CPU affinit y , are next to useless unless they are
co ordinated system wide. In a parallel application, for example, using explicit
CPU affinities a v oids running more than one task of that application on the
same CPU; ho w ev er, it do es not pro vide an y guaran tees regarding sim ultaneous
execution of tasks or exclusiv e access to CPUs. A solution for the problematic
1
2 CHAPTER 1. INTR ODUCTION
classes w ould b e to explicitly allo cate and free CPUs. But this p oses to o sev ere
restrictions on p ossible sc hedules for general purp ose computing systems with
m ultiple applications. Therefore, a more op erating system friendly solution lets
applications request certain sc heduling guaran tees, whic h are then honored b y
the op erating system sc heduler. This helps applications, whic h can no w rely on
a certain b eha vior, and it helps the op erating system, whic h has more degrees
of freedom with that. Such guaran tees could b e, for instance, a task getting a
certain amoun t of CPU time within a certain p erio d (for real-time tasks), or
a set of tasks alw a ys b eing executed sim ultaneously (for parallel applications).
The latter guaran tee is also kno wn as coscheduling or gang sc heduling, whic h is
the topic of the remaining thesis.
1.1 The Significance of Cosc heduling
Cosc heduling – roughly sp eaking the sim ultaneous execution of selected sets
of tasks – w as in tro duced on early parallel systems [1]. This “con text switc h
at system lev el” basically allo ws multiprogramming of parallel mac hines, while
eac h application still sees the whole mac hine as its o wn. Among other things,
this allo ws applications to use fine-grained comm unication efficiently . Because
comm unicating tasks are executed sim ultaneously , their pro cessing is not delay ed
b y unrelated tasks and timely answ ers are guaran teed. Esp ecially , it is p ossible
to use busy w aiting to sync hronize tasks, without burning CPU cycles w aiting
for tasks that are not ev en running. But cosc heduling is not only useful at
application lev el. In curren t researc h, it ev olv es in to a to ol, that is exploited
b y the op erating system itself to optimize the usage of shared resources, b y
considering whic h tasks should execute sim ultaneously .
Unfortunately , cosc heduling still carries the stigma of b eing not scalable,
costly to realize, and generally not w orth the effort, b ecause of fragmentation
problems and not enough adv an tages to b e gained from common applications.
And indeed, none of the curren t general purp ose op erating systems has imple-
men ted something ev en remotely similar to cosc heduling.
Ho w ev er, the hardw are and soft w are landscap e has c hanged substan tially in
the last decade and is still c hanging. And with this, the relev ance of cosc hedul-
ing c hanges. T o da y , parallel applications are seldom created from scratc h. In-
stead, they are usually dev elop ed on top of some parallel substrates, such as
Op enMP [2] or the Intel Thr e ading Building Blo cks [3], whic h encapsulate a lot
of the necessary parallel programming exp ertise. This causes more and more ap-
plications to b e parallel, as these en vironmen ts can also b e used b y dev elop ers,
who are less exp erienced in parallel programming, enabling them to pro duce sen-
sible results. A consequence of this is, that the question whether an application
w ould b enefit from cosc heduling has not to b e answ ered for eac h individual ap-
plications an ymore, but instead just for a few parallel programming substrates.
These few substrates could b e c hanged without to o m uc h time and effort to
exploit the b enefits of b eing cosc heduled.
1.2. PR OBLEM ST A TEMENT 3
In ev ery new pro cessor generation, eac h CPU shares more and more re-
sources with other CPUs. F or a pro cessor this means a b etter utilization of
its transistors; for a single task it means p oten tially less p erformance due to
other tasks “stealing” resources. While ev ery new hardw are generation still de-
liv ers more p erformance than the one b efore it, the p erformance gap b et w een a
non-optimized sc hedule and an optimized sc hedule also increases. While most
op erating system sc hedulers ha v e learned to place tasks somewhat in telligen tly
within mostly idle systems to reduce the resource con ten tion caused b y non-
optimal sc hedules, they do not go further than this. By not only con trolling
wher e a task is executed, but also when it is executed in relation to other tasks
within the system, it is p ossible to close this gap again. Ha ving to deal with an
increasing amoun t of parallel applications adds another la y er of complexit y .
1.2 Problem Statemen t
With resp ect to parallel applications, dev elopmen t of op erating system sc hed-
ulers has stagnated. Sc hedulers consider tasks only individually; they hav e no
notion of parallel applications. Bad sc heduling b eha vior on new arc hitectures
(e. g., when sim ultaneous m ultithreading (SMT) b ecame mainstream) is just
flimsily fixed and not addressed thoroughly . Though, this is not due to a lac k of
ideas. Rather, individual implemen tations of new ideas to solv e sp ecific issues
are often not fit for the requiremen ts of general purp ose op erating systems, or
they obliterate an y c hance of p eaceful co existence with other sc heduler impro v e-
men ts, reducing p ossible b enefits and applicabilit y .
Cosc heduling has the p oten tial to further dev elopmen t on all those fron ts:
parallel applications can profit from sim ultaneous execution, system design can
b e impro v ed or simplified b y utilizing cosc heduling, and resource managemen t
can b e optimized b y co ordinating sc heduling across m ultiple cores. Ho w ev er,
cosc heduling is also kno wn as a “scalabilit y nigh tmare w aiting to happ en” as it
w as describ ed b y P eter Zijlstra, one of the Linux sc heduler main tainers, on the
Lin ux Kernel Mailing List (LKML) in April 2010. A sen timen t that is shared
b y others.
This thesis reev aluates the concept of cosc heduling on con temp orary m ulti-
core systems and addresses the main dra wbac ks asso ciated with coscheduling.
It answ ers the follo wing questions:
1. What are the functional requiremen ts that are placed on a cosc heduler for
general purp ose m ulticore systems?
2. Ho w can suc h a cosc heduler b e realized without falling victim to non-
functional shortcomings?
3. Whic h hitherto unkno wn applications of coscheduling are p ossible on m ul-
ticore systems?
4 CHAPTER 1. INTR ODUCTION
1.3 Con tribution
By answ ering the aforemen tioned questions, this thesis mak es the follo wing pri-
mary con tributions to the state of the art:
1. A mo del for cosc heduling is dev elop ed that is sufficien t for all curren tly
kno wn use cases.
2. A cosc heduler design is deriv ed from functional requiremen ts, that exceeds
existing designs in terms of flexibilit y and v ersatilit y .
3. A metho d is pro vided that in tegrates said design in to existing general
purp ose sc hedulers without c hanging their c haracteristic traits.
4. The foundation of a managemen t concept is presen ted that unifies previ-
ously indep enden t domains of cosc heduling researc h.
5. Sev eral new use cases for cosc heduling are iden tified.
T ogether, these con tributions should remo v e an y inhibitions of sc heduler de-
v elop ers to in tegrate cosc heduling in to their pro duct. This in turn pav es the
w a y for man y use cases to mak e the transition from researc h (or nic hes) to real
life.
F urthermore, the follo wing secondary con tributions are made:
1. The first cross-domain surv ey of cosc heduling use cases on multicore ar-
c hitectures is made.
2. The generally accepted definition of cosc heduling is sligh tly generalized.
1.4 Structure of this Thesis
This thesis is split in to three parts, mo ving from foundations to design to the
application of cosc heduling.
The first part co v ers the foundations of coscheduling. First, Chapter 2 giv es
a thorough surv ey of cosc heduling use cases, co v ering all use cases disco v ered
to date as w ell as new ones disco v ered b y the author while preparing this the-
sis. These use cases are then dissected in Chapter 3 to dev elop a mo del for
cosc heduling and to in tro duce a common terminology . Chapter 4 finishes the
foundation b y taking a lo ok at existing cosc heduling approac hes and presen ting
them in ligh t of the newly gained insigh ts.
The second part is dedicated to the creation of a cosc heduler design suitable
for con temp orary m ulticore systems allo wing to realize the v arious use cases.
Design rationales driving the creation are motiv ated in Chapter 5, b efore the re-
sulting design itself is describ ed in Chapter 6. Its distinctiv e feature compared to
other cosc heduling solutions is its top ology-a w areness, which can refer to b oth,
1.5. CHAPTER NOTES 5
the organization of hardw are comp onen ts and the logical structure of soft w are.
Hence, the design is also referred to as T op ology-A w are Cosc heduling (T A CO).
After that, Chapter 7 fo cuses on w a ys to realize T A CO in already existing sc hed-
ulers without breaking their distinctiv e features. It gives t w o examples: Lin ux
and F reeBSD; with a sp ecific fo cus on the first one. The part closes with Chap-
ter 8, where basic asp ects of T A CO are ev aluated to demonstrate the fulfilment
of the design rationales.
The third part of the thesis addresses complex scenarios, where coscheduling
is applied. Chapter 9 compares differen t solutions for managing the sim ultane-
ous execution of man y parallel programs. I n this scenario, that will lik ely b e
commonplace in a few y ears, a solution based on T A CO is able to outp erform
established approac hes in most situations with the added b enefit of b eing bac k-
w ards compatible to applications that are not sp ecifically tailored to a managed
scenario. Chapter 10 demonstrates ho w cosc heduling can b e applied in recen t
pro cessors to mak e the automatic distribution of the energy budget (a. k. a. turb o
b o ost) more efficien t: in scenarios with jobs of v arying imp ortance, p erformance
is impro v ed consisten tly , while the energy consumption even gets reduced in cer-
tain cases. The last c hapter in this part, Chapter 11, details new design ideas and
design alternativ es for some traditional op erating system functionalities based
on cosc heduling. Sp ecifically , a concept based on T A CO is presen ted that allo ws
to reuse adv anced sc heduling optimization tec hniques – originally dev elop ed for
sequen tial applications – with parallel applications without ha ving to redesign
said tec hniques. A dditionally , the concepts of m utexes, affinities, and concur-
rency con trol are revisited and adv an tages and disadv an tages of their realization
with cosc heduling are discussed.
The thesis closes with Chapter 12, where the prosp ects of coscheduling are
assessed, taking in to accoun t the results of this w ork as w ell as curren t trends
in hardw are design and soft w are dev elopmen t.
1.5 Chapter Notes
In addition, eac h c hapter ends with chapter notes similar to this one. In them,
y ou will find some of m y p ersonal though ts p ertaining to eac h c hapter. These
can range from additional facts or anecdotes to a more sub jectiv e view on the
c hapter’s con ten ts. They pro vide a c hance to reflect on the presen ted con ten ts,
b efore the thesis mo v es on to another topic in the next c hapter.
6 CHAPTER 1. INTR ODUCTION
where use cases are presen ted, a mo del for co-
sc heduling is dev elop ed, and existing approac hes
are surv ey ed
P art I
Cosc heduling F oundation
7
T able of Con ten ts
2 Wh y Cosc heduling? 11
2.1 Application Design . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2 . 1 . 1 A c t i v e W a i t i n g ........................ 1 3
2.1.2 Sync hronous Execution . . . . . . . . . . . . . . . . . . . . 18
2.1.3 Arc hitecture-sp ecific Optimizations . . . . . . . . . . . . . 21
2.2 Resource Managemen t . . . . . . . . . . . . . . . . . . . . . . . . 22
2.2.1 Impro v ed Data Sharing . . . . . . . . . . . . . . . . . . . . 23
2.2.2 Reduced Resource Conten tion . . . . . . . . . . . . . . . . 24
2.2.3 Ph ysical Op eration . . . . . . . . . . . . . . . . . . . . . . 26
2 . 3 S y s t e m D e s i g n ............................. 2 7
2.3.1 Application Management . . . . . . . . . . . . . . . . . . . 27
2 . 3 . 2 S e c u r i t y ............................ 3 0
2.3.3 Device Management . . . . . . . . . . . . . . . . . . . . . 31
2 . 4 C h a p t e r N o t e s ............................. 3 3
3 Cosc heduling Mo del 35
3 . 1 B a s i c T e r m s .............................. 3 5
3 . 1 . 1 S o f t w a r e ............................ 3 6
3 . 1 . 2 H a r d w a r e ........................... 3 7
3 . 1 . 3 P a r a l l e l i s m ........................... 3 8
3.2 Cherry-pic king Cosc heduling T erms . . . . . . . . . . . . . . . . . 38
3.2.1 Ousterhout’s Coscheduling . . . . . . . . . . . . . . . . . . 38
3.2.2 F eitelson’s Gang Sc heduling . . . . . . . . . . . . . . . . . 39
3.2.3 Arpaci-Dusseau’s Implicit Coscheduling . . . . . . . . . . . 40
3.2.4 VMw are’s Relaxed Cosc heduling . . . . . . . . . . . . . . . 40
10 T ABLE OF CONTENTS
3 . 3 A s p e c t s o f T i m e ............................ 4 1
3 . 4 A s p e c t s o f S p a c e ........................... 4 4
3 . 5 C o s c h e d u l e d S e t s ........................... 4 6
3 . 6 T h e o r y v s . P r a c t i c e .......................... 5 0
3 . 7 C h a p t e r N o t e s ............................. 5 2
4 Cosc heduling Approac hes 53
4.1 Ousterhout’s Cosc heduling . . . . . . . . . . . . . . . . . . . . . . 53
4.2 Distributed Hierarc hical Con trol . . . . . . . . . . . . . . . . . . . 54
4.3 Distributed Queue T ree . . . . . . . . . . . . . . . . . . . . . . . . 56
4.4 Cosc heduling in VMw are ESXi . . . . . . . . . . . . . . . . . . . 56
4.5 F urther Considerations . . . . . . . . . . . . . . . . . . . . . . . . 57
4 . 6 C h a p t e r N o t e s ............................. 5 8
Chapter 2
Wh y Cosc heduling?
This c hapter giv es a thorough survey of use cases in whic h cosc heduling has b een
found useful on m ulticore arc hitectures. This c hapter fo cuses on why cosc hedul-
ing is b eneficial in certain scenarios. Differen t approac hes how cosc heduling can
actually b e ac hiev ed are discussed later in Chapter 4.
Cosc heduling can b e applied with differen t goals in mind (see Fig. 2.1). Usu-
ally these are optimizations of some kind: optimization of p erformance, energy
consumption, or energy efficiency . But there is also the goal to impro v e the
design of a piece of soft w are, e. g., mak e it simpler, less error-prone, or more
secure. T able 2.1 giv es an o v erview of all use cases presen ted in this c hapter.
This c hapter organizes the differen t coscheduling use cases b y their scop e
at whic h these use cases are usually applied. Section 2.1 presen ts use cases
fo cusing on individual (parallel) applications. Section 2.2 contains uses cases
whic h optimize the usage of resources of t ypically indep enden t tasks. As suc h,
these use cases are usually applied at the op erating system lev el, though parallel
applications ma y also b enefit. Finally , Section 2.3 surv eys use cases whic h target
sp ecifically the op erating system and its in terfaces.
P erformance
Design Energy
Figure 2.1: Areas, in whic h cosc heduling can induce b enefits.
11
12 CHAPTER 2. WHY COSCHEDULING?
T able 2.1: Summary of Cosc heduling Use Cases.
Use case Impro v es F or
A pplic ation Design
Fine-grained sync hronization Design Application
Scalabilit y of algorithms Design Application
Resp onse time P erformance Application
Lo c k holder preemption P erformance Application
Static load balancing Design Application
A uxiliary tasks Design App./System
Execution unit optimizations P erformance Application
Cac he optimizations P erformance Application
R esour c e Management
Memory pressure P erformance Application
Cac he pressure P erformance Application
Execution unit con ten tion P erformance Application
Cac he/memory bandwidth con ten tion P erformance Application
T emp erature balancing Energy/P erf. System/App.
P o w er capping Energy/P erf. System/App.
Energy con ten tion Energy/P erf. System/App.
System Design
P arallel application managemen t Design System
Concurrency managemen t Design/P erf. System/App.
Affinit y managemen t Design System
Side-c hannel elimination Design System
Exclusiv e device access Design/P erf. System/App.
Exp ose system functionalit y Design/Energy System
Sp ecial arc hitectures Design Hardw are
2.1. APPLICA TION DESIGN 13
t 1
t 2
🔒 🔒 🔒🔒🔒 🔒
🔒 🔒 🔒 🔒
(a) A ctiv e w aiting.
t 1
t 2
🔒 🔒 🔒 🔒 🔒 🔒
🔒 🔒 🔒 🔒
(b) P assiv e w aiting.
Figure 2.2: T w o tasks rep eatedly en tering critical sections in an otherwise idle
system. The activ e w aiting v arian t has a p erformance adv an tage o v er time due
to the extra time needed to w ak e up sleeping tasks.
2.1 Application Design
This section con tains use cases for cosc heduling, whic h ha v e a noticeable effect
on the design of applications – usually simplifying the design of an application in
some w a y . None of the presen ted use cases ha v e a negativ e impact on application
p erformance. When applicable, alternativ e design options to reac h the same goal
are discussed.
2.1.1 A ctiv e W aiting
W aiting for something is a building blo c k found often in soft w are engineering:
w aiting for a lo c k to b ecome free, w aiting for a message to arriv e, w aiting for
a result to b ecome a v ailable, etc. W aiting comes in t w o fla v ors: activ e w aiting
(a. k. a. busy w aiting or spinning), where the CPU activ ely and rep eatedly c hec ks
for the o ccurrence of an ev en t, and passiv e w aiting (a. k. a. blo c king), where the
curren t task is susp ended un til the ev en t in question is signalled b y another
comp onen t (e. g., another task or a hardw are in terrupt). A ctiv e w aiting is usually
considered as something that should b e a v oided, b ecause it preven ts the CPU
from doing useful w ork. How ev er, there are t w o situations where the use of
activ e w aiting o v er passiv e w aiting is indicated:
1. The exp ected w aiting time is less than the exp ected o v erhead of passiv e
w aiting, which not only includes con text switc hes but also cac he misses
due to the aforemen tioned con text switches and tasks that w ere executed
in the mean time.
2. There is nothing else to do, and the dela y of en tering and lea ving a p o w er
sa v e mo de is not acceptable. (Or there is no mec hanism in place to realize
passiv e w aiting.)
In an otherwise idle system, activ e w aiting allo ws to a v oid unnecessary
user/k ernel-space transitions and prev en ts dela ys from en tering and exiting a
14 CHAPTER 2. WHY COSCHEDULING?
t 1
t 2
(a) A ctiv e w aiting.
t 1
t 2
(b) P assiv e w aiting.
Figure 2.3: T w o tasks rep eatedly doing barrier sync hronizations in an otherwise
idle system. The activ e w aiting v arian t has less o v erhead.
deep er sleep state of the CPU. While the sa v ed time is rather small for a single
o ccurrence, it can accum ulate to measurable differences. This is illustrated in
Fig. 2.2, where t w o tasks rep eatedly en ter v ery short critical sections, e. g., to
up date a small lo c k protected data structure.
The figure sho ws the timeline of t w o tasks. As in other figures, time mo v es
from left to righ t and eac h horizon tal line usually sho ws a separate task and
its b eha vior while it is running. (Later figures ma y show individual CPUs and
whic h tasks they are curren tly executing.) Here, a lo c k sym b ol indicates that
a task is within a critical region; a small clo c k denotes a task that is activ ely
w aiting – in this case to en ter the critical region. In the case of passiv e w aiting,
the o v erhead of putting the CPU of a task to sleep and waking it up again is
hin ted at b y dark grey bars.
If w ait times are rather small, en tering a deep er sleep state is also coun ter-
pro ductiv e from an energy p ersp ectiv e, as CPUs migh t more often b e required
to w ak e up while still transitioning in to a sleep state. An example for this is
giv en in Fig. 2.3, with t w o tasks rep eatedly doing barrier sync hronizations. The
more balanced the w ork is, the more o v erhead is induced b y passiv e w aiting.
T o da y , most environmen ts are m ultiprogrammed, and applications usually
ha v e no con trol o v er when and where they are executed. Sp ecifically , a task can
b e preempted at an y time. This makes activ e w aiting at application lev el prob-
lematic, b ecause whatever ev en t a task is w aiting for, the generating task can b e
dela y ed arbitrarily . That is, it is imp ossible to predict the exp ected w aiting time
adequately . In the w orst case, the activ ely w aiting task is sc heduled on the v ery
same CPU as the ev en t generating task – essen tially blo c king its o wn progress.
Suc h unexp ected dela ys with activ e w aiting are illustrated in Figure 2.4, whic h
sho ws t w o tasks that rep eatedly p erform barrier sync hronizations, e. g., to signal
their readiness to b egin the next round of a parallel algorithm. In addition to
these tasks, there is also other load in the system, so that the displa y ed tasks
cannot run all the time. The short breaks in the timeline indicate p ossible skips
in time where solely other load is executed.
2.1. APPLICA TION DESIGN 15
t 1
t 2
(a) A ctiv e w aiting.
t 1
t 2
(b) P assiv e w aiting.
Figure 2.4: T w o tasks rep eatedly doing barrier sync hronizations in a system
that also sc hedules other tasks. The activ e w aiting v arian t causes considerable
o v erhead, whic h gets w orse with less time-slice o v erlap and shorter in terv als
b et w een barriers.
t 1
t 2
Figure 2.5: Same situation as in Fig. 2.4, but this time with activ e w aiting and
cosc heduling. The o v erhead of activ e w aiting is con tained and there are less
con text switc hes than in the passiv e w aiting case.
Fine-grained Sync hronization
Cosc heduling mak es activ e w aiting in m ultiprogrammed scenarios efficien t again.
In its simplest form, cosc heduling giv es an application the guaran tee of syn-
c hronous execution of its tasks. Thus, when a task is activ ely w aiting for an
ev en t generated b y another task, it is guaran teed, that this other task is cur-
ren tly b eing executed. F urthermore, should the other task get preempted for
some reason, the activ ely w aiting task will also b e preempted, so that no CPU
cycles are unnecessarily burn t. Figure 2.5 sho ws the previous scenario, but this
time the comm unicating tasks are cosc heduled. This is no w almost as if the
comm unicating tasks are executed in isolation. The one difference (whic h is not
prop erly reflected in the diagrams) is that ev ery time a task is sc heduled, the
cac he will ha v e to b e refilled, which causes additional slo w do wns compared to
an isolated execution. Note, how ev er, that the cosc heduled case results in few er
con text switc hes than in the non-cosc heduled passiv e w aiting case.
In the end, it dep ends on ho w fine-grained the sync hronization b et w een tasks
actually is, whether unco ordinated passiv e w aiting or cosc heduled activ e w aiting
is the more efficien t solution. The more fine-grained it is, the smaller is the
a v erage w aiting time and the more con text switc hes are sa v ed b y cosc heduled
activ e w aiting. The cross-o ver p oin t w as analyzed in more detail b y F eitelson
and R udolph in [4]. Though, they did not consider the effect of cac hes: to da y’s
con text switc h costs are dominated b y indirect costs due to cac he misses as
16 CHAPTER 2. WHY COSCHEDULING?
sho wn b y Liu and Solihin in [5] for sequen tial applications. This line of thinking
is pic k ed up later in Chapter 8 for parallel applications, where an upp er b ound
for the o v erhead of cosc heduling compared to isolated execution is deriv ed.
F or parallel applications or algorithms that already mak e use of fine-grained
sync hronization, for instance, b ecause they w ere though t to b e executed in iso-
lation, cosc heduling allo ws to execute them nearly without p erformance degra-
dation in m ultiprogrammed en vironmen ts. This mostly applies to t w o scenarios:
soft w are originally dev elop ed for high p erformance computing (HPC) en viron-
men ts and then reused on m ultiprogrammed systems without adjusting algo-
rithms prop erly , and soft w are originally executed on dedicated mac hines and
then migrated to virtualized en vironmen ts, where – while individual VMs ma y
still b e dedicated – m ultiple VMs are executed on the same system. A third
scenario is the execution of soft w are in en vironmen ts that are not as isolated as
they ough t to b e. This is seen for instance in HPC environmen ts, where activi-
ties of the op erating system (OS) – so called OS jitter – in terrupt and dela y the
execution of the curren tly pro cessed parallel application. While eac h individual
in terruption is rather short, esp ecially large scale collectiv e comm unications suf-
fer from this. Cosc heduling can b e used here to ac hiev e the desired p erformance
as demonstrated b y P etrini et al. in [6].
Scalabilit y of Algorithms
F or application dev elop ers, the prosp ect of efficien t fine-grained sync hronization
op ens the p ossibilit y to do a finer problem decomp osition than they would ha v e
done otherwise. Th us, smaller problems b ecome parallelizable and decomp osi-
tions ma y con tain more dep endencies than usual. Algorithms can b e scaled to
larger mac hines, b ecause subtasks ma y b e smaller.
Resp onse Time
A differen t issue is the resp onse time of parallel applications in m ultiprogrammed
scenarios, whic h migh t suffer with passive w aiting, b ecause of additional delays
b et w een w aking up a task (i. e., inserting it bac k in to the runqueue) and the
actual execution of that task. Ho w long that dela y is, strongly dep ends on
the load of the system and the emplo y ed scheduling strategy . Usually , sc hed-
ulers giv e in teractiv e tasks some kind of preferen tial treatmen t, whic h – at least
theoretically – w ould k eep resp onse times lo w. Ho w ev er, simple classification
strategies migh t mistak e non-in teractiv e parallel applications with fine-grained
sync hronization as in teractiv e, when they use passive w aiting, due to their short
pro cessing phases. This w ould n ullify any improv emen t of resp onse times of
truly in teractiv e applications. (This is demonstrated in a sligh tly differen t con-
text b y Xu et al. in [7].) In suc h a situation, ev en applications with passiv e
w aiting ma y profit from b eing cosc heduled.
2.1. APPLICA TION DESIGN 17
v C P U 1
v C P U 2
🔒
🔒
🔒
🔒
🔒
🔒
Figure 2.6: Lo c k holder preemption. Although ev ery lo c k is only held for a short
p erio d in terms of task run time, a preemption can prolong the actual hold time
arbitrarily .
Lo c k Holder Preemption
The curren tly most prev ailing problem, ho w ev er, are virtual mac hines with m ul-
tiple (virtual) CPUs, i. e., SMP guests executing on SMP hosts. F ully virtualized
SMP guests are a prime example for a piece of parallel soft w are that can not b e
redesigned so that is do es not mak e use of activ e w aiting: practically ev ery OS
for m ulticore systems mak es use of spinlo c ks for lo w-lev el sync hronization. On
real hardw are, this is not a problem as a lo c k is only held for a v ery short amoun t
of time. In a virtualized en vironmen t, ho w ev er, a CPU of a guest – a so-called
virtual CPU (vCPU) – is represen ted as a thread in the host system, whic h can
b e preempted at an y time. The effect has b een aptly named lo ck holder pr e emp-
tion (LHP) , whic h is illustrated in Fig. 2.6. (Strictly sp eaking, the term LHP
could also b e used for most of the previously giv en examples when using the
more general definition of lo ck as known from lo c k-free algorithms [8].) With
the curren t usage of virtualization in cloud computing en vironmen ts, LHP has
b ecome a real problem, with lots of p eople trying to alleviate it in one w a y or
the other.
Practically used solutions to defuse LHP in virtualization settings include
cosc heduling in VMw are vSphere [9], para virtualized spinlo c ks in Lin ux [10],
and pause lo op exiting (PLE) in In tel pro cessors [11] or pause filtering (PF)
in AMD pro cessors [12]. Other tec hniques include dela y ed preemption [13],
preemptable tic k et lo c ks [14], and lo c k-free op erating systems [15]. T able 2.2
giv es a short o v erview o v er these approache s. Without going in to detail, eac h
approac h has its dra wbac ks: All except coscheduling, PLE/PF, and dela y ed
preemption via observ ation require mo difications of the guest OS, whic h are not
alw a ys p ossible. Preemptable tic k et lo c ks, while b eing a guest-only solution,
do not fully solv e LHP (tic k et lo c ks are merely degraded to regular spinlo cks,
when the next task in the queue is not running). Delay ed preemption m ust
also b e protected against abuse b y a guest; the v arian t with observ ation is also
rather coarse-grained as it mak es decisions on whether a vCPU is in k ernel mo de
or user mo de. Para virtualization (PV) of spinlo c ks as done in Lin ux results in
passiv e w aiting seman tics – whic h includes the resp onse time problem men tioned
ab o v e. The hardw are assisted solutions PLE/PF are seman tically equiv alen t to
spin-blo c king (w aiting activ ely for a momen t b efore blo c king), but con trary to
18 CHAPTER 2. WHY COSCHEDULING?
T able 2.2: Approac hes to handle LHP . Man y of these can also b e applied outside
of virtualization settings – then host and guest ha v e to b e substituted by OS
and application, resp ectiv ely .
Approac h Mo difications Result
Cosc heduling host only useless w aiting a v oided
P ara virtualized spinlo c ks host, guest w aiting a v oided
P ause lo op exiting/
pause filtering
host, hardw are w aiting truncated,
requires virtualization
Dela y ed preemption (via PV) host, guest LHP largely a v oided
Dela y ed preemption
(via observ ation)
host only LHP largely a v oided,
requires virtualization
Preemptable tic k et spinlo c ks guest only w aiting partly a v oided
Lo c k-free algorithms guest only LHP non-existen t
spin-blo c king they do not con v ey information ab out unblock ed vCPUs, whic h
requires guessw ork in the host to decide whic h vCPU should b e executed next.
Cosc heduling and PLE/PF are the only solutions that also w ork, when the actual
lo c k holder is not the guest OS itself but a task of a parallel application within
the guest (assuming that LHP within the guest itself is not a problem). This
mak es cosc heduling the only generally applicable solution, when not emplo ying
lo c k-free algorithms – whic h are still elusiv e for most dev elop ers – in all guests.
While the guaran tees pro vided b y cosc heduling migh t b e to o strong for some
applications, it should also b e noted that this form of LHP handling requires
almost no information ab out the executed applications at system lev el. It is suffi-
cien t to sp ecify groups of tasks whic h migh t w ait for eac h other. Ho w ev er, with
additional information cosc heduling can b e applied more fine-gran ular, whic h
mak es this solution also suitable for applications whic h do not profit from b eing
cosc heduled otherwise. This is explored later in Chapter 11.
2.1.2 Sync hronous Execution
The guaran tee of sync hronous execution that is pro vided b y cosc heduling is quite
strong. While it enables efficien t fine-grained sync hronization, it also pro vides
b enefits in other scenarios. P articularly , sync hronous execution of tasks implies
that these mak e progress equally fast. This lesser guaran tee is useful in the
area of load balancing. The full guarantee is needed to realize certain classes of
auxiliary tasks.
2.1. APPLICA TION DESIGN 19
Static Load Balancing
In order to mak e full use of a dedicated parallel mac hine, it is go o d practice
to balance the computational load ev enly across all a v ailable CPUs. This can
b e either done dynamically at run time or statically already at design time [16].
Whic h one should b e preferred, dep ends on sev eral differen t factors. Dynamic
load balancing has the adv an tage of flexibilit y . It can handle almost an y sit-
uation, esp ecially situations with v arying c haracteristics, suc h as subtasks of
v arying complexit y . Ho w ev er, it comes with o v erhead at run time: calculation
of load, handling of w ork queues, and p ossibly migration of subtasks. Static
load balancing is less flexible, but has no o v erhead at runtime. In the b est case,
there is no need for comm unication b et w een CPUs: eac h CPU just pro cesses
its part of the problem. Static load balancing is for instance practiced in the
HPC area with MPI [17]. Another example is Op enMP [2], where iterations of a
parallel lo op are also distributed statically b et w een tasks b y default. While not
really static, parallel virtual machines (VMs) can b e seen as another example:
the sc heduler of the guest OS usually assumes that eac h of its vCPUs deliv ers
constan t computational p o w er, and balances load accordingly .
If the parallel system is also used for unrelated computations, static load
balancing b ecomes a burden: ev ery unrelated computation on a CPU causes a
dela y . A t the end of the parallel job, most tasks will ha v e finished, except for
those that ha v e b een dela y ed. This happ ens, b ecause OS sc hedulers usually only
pro vide a guaran tee of fairness and not a guarantee of equal progress. Hence,
ev en small, transien t disparities in fairness ma y lead to a large difference in
receiv ed CPU time, ev en tually . And sc heduling algorithms normally do not giv e
tasks a c hance to catc h up CPU time. With cosc heduling, equal progress is
enforced and static load balancing can b e used within parallel applications for
m ultiprogrammed en vironmen ts as sho wn in Fig. 2.7.
A uxiliary T asks
The guaran tee of sync hronous execution also op ens a new design option in m ul-
tiprogrammed scenarios: auxiliary tasks that augmen t the functionalit y of the
main task in some w a y . Con trary to parallel algorithms, where multiple tasks
w ork co op erativ ely on a problem, auxiliary tasks are more or less optional. P o-
ten tial tec hniques for auxiliary tasks include prefetching [18, 19], sp eculativ e
execution [20], and analysis [21, 22].
Prefetc hing auxiliary tasks are tasks that prefetc h memory for the main task
in to a shared cac he, so that the main task exp eriences as few stalls as p ossible.
This is b eneficial, when the data access pattern is irregular and not detected b y
hardw are prefetc hers (e. g., p oin ter c hasing). With task-lev el sp eculation auxil-
iary tasks start with some calculations early , without b eing sure that their input
v alues are the correct ones. Ideally , it turns out that the input v alues ha v e not
c hanged, when the main task finally requires the results of the sp eculativ e execu-
tion. If a missp eculation is detected, the results of an auxiliary task are simply
20 CHAPTER 2. WHY COSCHEDULING?
t 1
t 2
(a) Without cosc heduling.
t 1
t 2
(b) With cosc heduling.
Figure 2.7: An application with t w o tasks and static load balancing in a system
with an o dd n um b er of tasks. Without cosc heduling, the im balance will cause
one task to mak e less progress than the other; and it strongly dep ends on the
sync hronization frequency within the application and a p oten tial rebalancing
in terv al, whether the unev en progress has a c hance to ev en itself out or not.
With cosc heduling, the im balance is still there but do es not affect the application
an ymore.
discarded and the calculation is redone. Finally , auxiliary tasks can b e used for
analysis instead of p erformance impro v emen ts. The adv an tage of realizing the
analysis of a main task with separate tasks is that the main task do es not need
to ha v e costly instrumen tation, i. e., it executes with its original p erformance
despite b eing analysed. The analysing can b e direct, such as monitoring mo di-
fications of data structures and stac k, or indirect, suc h as lo oking for b eha vioral
c hanges when shared resources are more or less stressed b y one or more auxiliary
tasks.
The sync hronous execution pro vided b y cosc heduling allo ws to use these
tec hniques also in m ultiprogrammed scenarios, where tasks comp ete for compu-
tational resources. Most of these tec hniques also dep end on prop erties of the
underlying system, and th us require a certain placemen t relativ e to the main task
within the parallel system. Prefetc hing requires a shared cac he, sp eculation is
lik ely to profit from a shared cac he, and analysis requires sharing of resources
dep ending of what actually is analysed.
Sp ecifically for the purp ose of analysis, coscheduling also pro vides the guar-
an tee, that other tasks will not b e able to influence the analysed task. That is,
cosc heduling pro vides a con trolled en vironmen t in a dynamically used system.
Prefetc hing and sp eculation trade computational resources for a bit more p er-
formance. Dep ending on the effectiv eness, it migh t mak e sense from a system
p ersp ectiv e not to emplo y these tec hniques, when enough load is a v ailable within
the system. These trade-offs are discussed in Chapter 9. A mechanism to enable
the op erating system to mak e suc h decisions, whic h can also b e applied to task
p o ols in general, is explored later in Chapter 11.
2.1. APPLICA TION DESIGN 21
2.1.3 Arc hitecture-sp ecific Optimizations
One facet of soft w are optimization is to tune soft w are to w ards the system or
systems where it is going to b e deplo y ed, so that the soft w are is able to utilize
the target system to its fullest p oten tial. T o da y , this is particularly done in the
HPC area and in m ultimedia applications. (In some sense, VMs are also highly
optimized. The difference is, that they do all their optimization to w ards the
target system at run time.)
While a tremendous amoun t of target sp ecific optimization is done b y the
compiler itself, the compiler cannot do ev erything. Esp ecially , automatic paral-
lelization is – except for sp ecial cases – an as of y et mostly unsolv ed problem.
Usually the dev elop er is resp onsible for high-lev el optimization and paralleliza-
tion, while the compiler co v ers lo w-lev el asp ects – though the b oundaries are
blurred. In the end, it is a sym biotic pro cess with the dev elop er pro viding suit-
able input, whic h is then turned in to something that will execute efficien tly on
the target system.
With resp ect to m ulticore arc hitectures and cosc heduling, t w o optimization
tec hniques are of particular in terest: optimization for execution unit o ccupancy
and optimization of cac he utilization. Both target resources, whic h are p oten-
tially shared b et w een CPUs. F or these and also other shared resources, optimiza-
tion differs dep ending on whether y ou do or do not kno w, what other CPUs are
executing. Cosc heduling giv es parallel applications this kno wledge, no matter
what else is executed in the system. Th us, cosc heduling mak es it p ossible to use
established metho ds of parallel application optimization without risking that
these optimizations migh t w ork against them as it w ould b e p ossible without
cosc heduling.
On the other hand, if it turns out that tasks of a parallel application are sim-
ply a bad matc h for a certain shared resource, a cosc heduling capable op erating
system will also b e able to a v oid cosc heduling of these tasks to some extend and
find b etter tasks to execute them with (see also Section 2.2).
Execution Unit Optimizations
If CPUs share some of their execution units with other CPUs, i. e., the system has
supp ort for sim ultaneous m ultithreading (SMT), this should b e considered b y
optimized parallel applications. Consider, for example, a system with pro cessors
similar to AMD’s Bulldozer microarc hitecture [23], where eac h CPU has its o wn
in teger execution unit but has to share a floating p oin t execution unit with a
second CPU. Assume further, that in this system eac h execution unit is able to
finish one instruction p er clo c k cycle. An optimized single-threaded application
will issue one in teger and one floating p oin t op eration p er cycle to fully utilize
the execution units of its CPU. This is ideal, when the shared floating p oint unit
is not used b y the other CPU. Ho w ev er, imagine a task optimized in this w a y
within a parallel application: t w o tasks w ould no w comp ete for the floating p oin t
unit, making it the b ottlenec k. This in turn causes a reduced utilization of the
22 CHAPTER 2. WHY COSCHEDULING?
in teger units. Within a parallel application on this particular system, it w ould
b e ideal for ev ery task to issue one in teger op eration ev ery cycle and one floating
p oin t op eration ev ery second cycle. This w ould result in a fully utilized system
with no particular b ottlenec k. (This assumes that tasks do similar things. There
is also the option to mak e use of execution units asymmetrically . F or the CPU
describ ed ab o v e, this w ould b e one task with only in teger op erations for ev ery
task optimized as in the sequen tial case.)
Cac he Optimizations
Cac he optimizations are w ell known from sequen tial applications. They are
imp ortan t b ecause of the large p erformance difference of cac he and main memory
and the p oten tial impro v emen ts that can b e ac hiev ed. Most tec hniques target
the data pro cessing lo ops of applications, mo difying data tra v ersal and access
patterns to b e more suitable to the target arc hitecture. With the p olyhedral
mo del exists an approac h to solv e certain cases programmatically (e. g., [24]).
Another approac h are auto-tuning mec hanisms, where several v ariations of an
algorithm are executed to determine the b est v arian t exp erimen tally (e. g., [25]).
Multicore arc hitectures, where cac hes are shared b et w een CPUs, add another
lev el of complexit y to cac he optimizations: the p erformance of a task no longer
dep ends just on its o wn handling of the cac he; what tasks on other CPUs are
doing in the shared cac he will also affect the task’s p erformance. Th us, parallel
applications m ust tak e this in to accoun t and distribute their w ork, so that tasks
running on CPUs with shared cac hes alw a ys w ork on similar parts of the problem
(e. g., [26]). Once the algorithm for that is in place, the finer details can b e again
determined b y auto-tuning. The in v olv ed pro cesses are demonstrated using the
example of parallel stencil computations in [27].
A differen t approac h to handle cache optimizations are cac he oblivious al-
gorithms [28], whic h are designed to w ork quite w ell with whatev er amoun t of
cac he is a v ailable without the need to tune an y parameters: they usually w ork
on progressiv ely smaller amoun ts of data, which will fit in to the cac he at some
p oin t. On systems with shared cac hes, tasks using cache oblivious algorithms
ha v e a clear adv an tage o v er statically cac he optimized tasks in that they degrade
more gracefully as so on as other CPUs also use the shared cac he. Ho w ev er, they
neither allo w to predict the p erformance loss of individual tasks, when differen t
cac he oblivious algorithms use a shared cac he sim ultaneously , nor do they solv e
the handling of shared cac hes within parallel applications. F or the latter, one
can still gain adv an tages b y letting tasks w ork on o v erlapping data sets, so there
is less comp etition for the cac he.
2.2 Resource Managemen t
If arc hitecture-sp ecific optimizations as describ ed in the previous section cannot
b e done – for instance, b ecause the final application mix w as not kno wn at design
2.2. RESOUR CE MANA GEMENT 23
time or some piece of soft w are cannot b e recompiled for a differen t system – the
op erating system (or run time library) can try to manage tasks, so that the usage
of system resources is optimized. This often means a v oiding situations where
m ultiple tasks w an t to use the same resource at the same time. This resource
con ten tion usually results in a decreased p erformance for at least one task. F or
man y in ternal system resources, where access is managed automatically b y the
hardw are itself, sim ultaneous access causes short stalls in the execution of tasks.
Examples include shared cac hes (where ev en the con ten ts are managed in hard-
w are), execution units in a SMT system, memory bandwidth, and others. These
stalls cannot b e mask ed b y con text switc hes, as it is customary for instance for
I/O devices in an op erating system. Th us, the time sp end stalling is effectiv ely
lost. (Paging of main memory has a sp ecial role as it is one of the few cases
where the op erating system activ ely manages access to system resources and
is able to mitigate the o v erall effect of stall with con text switc hes to a certain
degree.)
By con trolling, whic h tasks are executed sim ultaneously and whic h are not,
the op erating system can reduce and sometimes ev en a v oid the o ccurrence of
suc h stalls. This has b enefits for the system itself, whic h suddenly realizes more
instructions p er cycle, and for applications which exhibit lo w er resp onse times on
a v erage. T o da y’s m ulticore pro cessors ha v e a plethora of shared resources. They
can b e split in to t w o broad categories: resources holding data, suc h as cac hes and
main memory , whic h are limited b y their capacit y; and resources transp orting
data, whic h are mainly defined b y their bandwidth (and their latency , though it
is not imp ortan t for the discussion here).
2.2.1 Impro v ed Data Sharing
Shared memory , whether it is system memory or some cac he, is limited. The
more tasks are running, the higher is the com bined demand. When the com bined
demand exceeds the a v ailable memory , the memory is effectiv ely time-shared
b y rep eatedly replacing cac he lines or memory pages. This migh t result in a
substan tially decreased p erformance, b ecause the hardw are (for cac he) or the
op erating system (for system memory) p ermanen tly has to fetc h some piece of
memory . This effect is called thr ashing [29].
Memory Pressure
F or system memory , thrashing means that a task – as so on as it is executed –
runs in to a page fault and requires a page to b e sw app ed in. This puts stress
on the I/O subsystem, whic h b ecomes the b ottlenec k and limits p erformance.
The traditional solution is to reduce the n um b er of applications, which reside
in memory sim ultaneously , so that their com bined w orking sets still fit in to
memory [29]. This approac h can also b e used with cosc heduling as the ideas are
mostly orthogonal [30].
24 CHAPTER 2. WHY COSCHEDULING?
Ho w ev er, carefully applied cosc heduling reduces the need for suc h explicit
t w o-lev el sc heduling sc hemes in parallel systems. Firstly , cosc heduling b y itself
when applied to parallel applications reduces the m ultiprogramming degree nat-
urally: at an y p oin t in time there are only a few parallel applications running
sim ultaneously . This reduces the com bined w orking set across all CPUs in a sys-
tem compared to unco ordinated sc heduling. Secondly , with some coscheduling-
a w are paging p olicies the paging activities no longer influence the running appli-
cations negativ ely [31]. Finally , the con trol of the op erating system can b e used
to prefetc h the w orking set of the applications that are going to b e executed
next, so that all necessary sw apping can b e handled in the bac kground [32].
The dra wbac k here is that this st yle of cosc heduling requires larger time-slices
than those t ypically used to da y , though it is not w orse than those of t w o-lev el
sc hemes.
Cac he Pressure
F or shared cac hes in a m ulticore pro cessor thrashing can mak e a cache ineffectiv e:
in the w orst case ev ery cac he lo ok-up fails and the next lev el of cac he or the
system memory m ust b e consulted. Similar to system memory , cosc heduling can
b e used to select tasks to execute sim ultaneously , so that their com bined w orking
set gets the most b enefit from the shared cac he. Ho w ev er, for caches it is more
complicated to deriv e w orking set sizes b ecause of the transparen t managemen t
of cac hes. A dditionally , other arc hitectural details ha v e to b e tak en in to account
– cac he asso ciativit y for instance.
Still, with enough information it is p ossible to predict ho w a particular com-
bination of tasks will p erform when cosc heduled on CPUs with a shared cac he.
T o gather this information, tasks are executed in more or less con trolled situa-
tions in order to generate profiles of individual tasks (e. g., [33]) or com binations
of tasks (e. g., [34]). Once this data is a v ailable, appropriate groups can b e
formed. Most approac hes, ho w ev er, do not consider cac he con ten ts directly . In-
stead, they simply select com binations that reduce the o v erall n um b er of cac he
misses as this is an easily retriev ed measuremen t. Approac hes, whic h fo cus on
reducing the amoun t of data transferred, are discussed in the next section.
One example that directly targets cac he con ten ts is the w ork b y T am et
al. [35]: they use p erformance coun ters to detect whether cac he misses w ere
resolv ed b y remote cac hes. If so, tasks accessing the same data are not executing
close together and cosc heduled groups are mo dified to rectify that.
2.2.2 Reduced Resource Con ten tion
F or resources with access directly managed b y the hardw are itself, sim ultaneous
accesses b y m ultiple pro cessor cores lead to stalls in the execution of tasks.
These resources range from instruction fetc hing and execution unit allo cation in
SMT systems o v er accessing a shared cac he to accessing off-c hip resources, e. g.,
2.2. RESOUR CE MANA GEMENT 25
memory or I/O-devices. In most cases, access to hardware is arbitrated in a fair
manner, giving eac h CPU an equal share of access opp ortunit y . (One exception
is, for instance, the SMT realization in PO WER pro cessors, where hardw are
threads can b e prioritized [36].)
F or devices or links that can b e saturated b y a single CPU, this leads to a
w orst case effect of losing an y adv an tage of ha ving m ultiple CPUs. The only
w a y to surly a v oid this kind of resource con ten tion, is to ha v e at most one
task executing whic h accesses a particular resource. In most cases, this is not
a sensible option as tasks are usually comp osed of v arying mixes of resource
accesses, whic h naturally o v erlap.
Execution Unit Con ten tion
On systems with shared execution units, suc h as SMT systems where all execu-
tion units are shared or other systems where just some execution units are shared
(the FPU for instance), it is b eneficial to execute tasks sim ultaneously , that do
not use the same shared execution units at the same time. F orm ulated differ-
en tly: sim ultaneously executed tasks should complemen t eac h others’ resource
usage. This w a y , stalls while waiting for execution units (or other resources
related to instruction execution) to b ecome a v ailable are minimized.
The information that is necessary to mak e the required decision can usu-
ally b e gathered from p erformance counters directly – either b y observing and
assessing com binations of tasks selecting only go o d sets to cosc hedule after a
sample phase [37], or b y assessing metrics of individual tasks and deciding on
whic h to com bine [38] Here, it w orks w ell to cosc hedule in teger in tensiv e with
floating p oin t in tensiv e applications; or compute in tensiv e and memory in tensiv e
applications: while the former are able to utilize most execution units at once
when highly optimized, the latter mak e use of them only ev ery few cycles and
w ait for data from cac he or memory most of the time. Ho w ev er, as so on as
cac he and memory accesses are considered, there is no longer a clear distinction
b et w een this and the next use case.
Memory and Cac he Bandwidth Con ten tion
If CPUs share the same link to their cac he or memory , cosc heduling can b e
used to reduce con ten tion on these links b y selecting appropriate com binations
of tasks to execute sim ultaneously on CPUs sharing certain links. Zh ura vlev et
al. [39] giv e an o v erview o v er differen t tec hniques ac hieving this.
Most tec hniques qualify the memory in tensiv eness of tasks b y utilizing cac he
miss information in some w a y , whic h roughly corresp onds to the amoun t of data
transferred b et w een t w o la y ers of the memory hierarch y . Then, some memory
in tensiv e tasks are cosc heduled with some less memory in tensiv e tasks, so that
the a v ailable memory bandwidth is hop efully w ell distributed among the mem-
ory in tensiv e tasks. Ideally , the com bined demand do es not ev en exceed the
a v ailable bandwidth. There is also a sligh t o v erlap with the cache pressure use
26 CHAPTER 2. WHY COSCHEDULING?
case, b ecause a combination of tasks migh t exhibit an ev en higher demand of
bandwidth due to thrashing at an in termediate shared cac he.
2.2.3 Ph ysical Op eration
In addition to the actual ph ysical pro cessor resources, there is also the less
tangible resource energy , whic h has b ecome a problem in recen t y ears. The
more p o w er a system consumes, the more heat m ust b e dissipated, so that the
system is k ept in an op erational state.
T emp erature Balancing
When the co oling budget in a computer system is limited, sp ecial care has
to b e tak en, so that the system do es not reac h unhealth y temp erature lev els.
With resp ect to pro cessors, traditional approac hes (see [40] for a surv ey) include
reductions in op erating v oltage and frequency as w ell as the forcibly insertion of
idle cycles. In a partly loaded m ulticore system, it is also p ossible to shift the
load around, so that all pro cessor cores are equally heated.
Going further, one can exploit that ev ery task has its o wn energy c harac-
teristic: highly optimized tasks generally utilize execution units b etter; th us,
they consume more energy and generate more heat than less optimized tasks.
Considering a single CPU, it mak es sense to in terlea v e execution of suc h hot
and c old tasks. On m ulticore systems, cosc heduling pro vides the natural exten-
sion of this line of though t: b y ensuring that hot and cold tasks are executed
sim ultaneously on close CPUs (in addition to in terleaving hot and cold tasks
on individual CPUs), the o v erall heat dev elopmen t can b e lo w ered (e. g., [41]).
This dela ys or ev en av oids the p oin t where additional mec hanisms to address
temp erature hotsp ots ha v e to b e emplo y ed.
P o w er Capping
Ev en when co oling is not a problem, the p o w er consumption itself migh t still b e
and the OS or hardw are has to ensure that certain p o w er lev els are not exceeded
b y limiting the ac hiev able frequency/v oltage. Here, the op erating system can
exploit the individual energy c haracteristics of tasks and a v oid to cosc hedule
energy-h ungry tasks (or enforce cosc heduling of more and less energy-in tensiv e
tasks). This w a y , it ma y b e p ossible to sta y within a giv en p o w er en v elop e at a
higher frequency than it w ould b e with an unco ordinated sc hedule.
Note, that it do es not matter whether p ow er consumption is capp ed in soft-
w are (e. g., via A CPI P-states) or in hardw are (e. g., via the R unning A ver age
Power Limit (RAPL) feature a v ailable in recen t In tel Pro cessors) – or whether
the frequency/v oltage is adjustable at a p er-core or only at a p er-so c k et lev el. In
all cases, cosc heduling enables the op erating system to a v oid p eaks in the p o w er
consumption if the w orkload is v aried enough.
2.3. SYSTEM DESIGN 27
Energy Con ten tion
If the distribution of the energy budget is managed b y the pro cessor itself (i. e.,
the pro cessor supp orts Intel T urb o Bo ost T e chnolo gy , AMD T urb o Cor e T e chnol-
o gy , or a similar technique), the pro cessor automatically puts most of the energy
budget to go o d use, e. g., by transparen tly increasing the frequency of the re-
maining cores when some cores transition in to a sleep state or b y transparen tly
decreasing the frequency when a core executes particularly demanding instruc-
tions. Ho w ev er, due to this dynamically shared energy budget, the execution of
a task on one core no w affects the p erformance of tasks executed on other cores,
slo wing them do wn b y con tending for the shared resource energy . Coscheduling
giv es the op erating system the abilit y to regain con trol ab out whic h tasks are
allo w ed to b e influenced b y other tasks and whic h tasks should execute at p eak
p erformance. This use case is explored in detail later in Chapter 10.
2.3 System Design
In this section, cosc heduling use cases are presen ted, whic h influence either
the design of the op erating system itself or extend or simplify the user/k ernel-
in terface.
2.3.1 Application Managemen t
T raditionally , cosc heduling w as the answ er to the question ho w m ultiple parallel
applications – where tasks hea vily profit from sync hronous execution – can b e
executed without ha ving to resort to batc h pro cessing [1]. And while the man-
agemen t of suc h parallel applications is certainly one asp ect, it is certainly not
the only one since to da y’s parallel applications tak e man y differen t forms and
shap es.
P arallel Application Managemen t
Cosc heduling allo ws a simple elev ation of parallel applications to first class cit-
izens. Instead of sc heduling tasks, the op erating system now sc hedules appli-
cations. CPU time can b e managed at application-lev el instead of task-lev el,
accoun ting for the fact that some applications are more parallel than others.
This stops the selfish “more is b etter” men talit y of some applications, whic h
spa wn tasks despite diminishing returns. With adequate accoun ting, an appli-
cation creating tasks b ey ond its optimal n um b er will only h urt itself.
Also, to da y’s parallel applications are often moldable or ev en malleable as
defined b y F eitelson and R udolph [42], giving the op erating system a more activ e
role in the managemen t: for moldable applications, the op erating system can
select the degree of parallelism at startup (for example, MPI applications often
b elong in to this category), while malleable applications can ev en b e reconfigured
28 CHAPTER 2. WHY COSCHEDULING?
at run time. Compared to sc heduling approac hes, whic h purely partition a system
in space, cosc heduling adds partitioning in time as a new dimension to the
solution space. This added flexibilit y can b e utilized in differen t w a ys (e. g.,
[43 – 45], Chapter 9). In particular, cosc heduling has the abilit y to reduce the
managemen t o v erhead and allo ws easy handling of w orkloads with non-malleable
applications in the mix. A more in-depth discussion of this use case can b e found
in Chapter 9.
A dditionally , Chapter 11 div es in to a more sp ecific example, where algo-
rithms to a v oid resource con ten tion are no w applied to parallel applications
instead of individual tasks.
Concurrency Managemen t
Bridging the areas of application and system design, cosc heduling can b e used
to con trol the n um b er of related tasks that are executed sim ultaneously at an y
p oin t in time. If the execution of related tasks is not co ordinated, the exp ected
n um b er of sim ultaneously executing related tasks is related to the o v erall n um b er
of runnable tasks in the system: the more tasks there are, the higher is the
probabilit y that related tasks are not executed sim ultaneously . That is, the
exp ected degree of concurrency within a group of related tasks decreases with
more tasks and increases with less. Cosc heduling allo ws to con trol this degree
explicitly and indep enden tly from the actual n um b er of runnable tasks within a
group.
Limiting the n um b er of sim ultaneously executing tasks in a group (without
limiting their o v erall n um b er) can b e used to lo w er con ten tion for shared data
structures. When the ov erall load is large enough, the mac hine can still b e
op erated at full utilization. If these shared data structures are accessed with
blo c king algorithms, tasks will blo c k less often – though lo c k holder preemption
migh t need to b e handled separately . F or lo c k-free algorithms, limiting the
n um b er of sim ultaneous executing tasks results in less retries. Obstruction-free
algorithms will ab ort less often and can ev en b e brough t out of a liv elo c k, if time
slices are long enough. This is of particular in terest for transactional memory
systems, as they fall mostly in to the obstruction-free category [8, 46].
Figure 2.8 illustrates suc h a case with four task optimistically trying to
c hange a shared data structure. When a task recognizes that another task has
mo dified the data structure in the mean time, it simply tries again. In a system
without an y sc heduling co ordination across CPUs, it is up to chance whether w e
will see the b est case (Fig. 2.8a), the w orst case (Fig. 2.8b), or – more lik ely –
something in b et w een. Cosc heduling can enforce either situation; the b est case
b y forbidding sim ultaneous execution. Ho w ev er, if there is not enough load, it
migh t b e b etter to still allo w t w o of those tasks to execute sim ultaneously to
a v oid an idle system.
The other w a y around, sim ultaneously executing as man y related tasks as
p ossible is the normal mo de of op eration for cosc heduling. This can also b e used
in a sligh tly mo dified v arian t, where not all tasks ha v e to execute sim ultaneously
2.3. SYSTEM DESIGN 29
t 1
t 2
t 3
t 4
(a) Best case (at least nearly , some in tert wining is still p ossible).
t 1
t 2
t 3
t 4
(b) W orst case.
Figure 2.8: F our tasks optimistically up dating a shared data structure; retrying
when another task w as faster. Without cosc heduling, an y sc hedule is p ossible.
Explicitly limiting the degree of concurrency with cosc heduling enforces the b est
case.
but at least a certain n um b er of them (when there are more tasks than that),
giving the OS sc heduler a little bit more flexibilit y . A high degree of concurrency
is useful for certain classes of parallel algorithms that actually w ork more effi-
cien t with more sim ultaneously running tasks, suc h as soft ware com bining and
elimination [8].
This use case is co v ered in more detail in Chapter 11.
Affinit y Managemen t
Cosc heduling also remo v es man y of the reasons wh y applications manage task
affinities b y themselv es. T ypically , the top ology of the system is someho w im-
p ortan t for an application, for example to realize some arc hitecture-sp ecific op-
timizations. Another reason for direct affinit y managemen t is to a v oid load
balancing artifacts of the op erating system sc heduler, whic h migh t ev en o ccur
in dedicated scenarios: if a set of tasks b ecomes runnable, it is not necessarily
spread out among the CPUs. The tasks m ust at first generate load, so that the
load balancer will consider them. Th us, a parallel application migh t not realize
its full p oten tial un til the balancer did its job.
Cosc heduling naturally solv es the latter: runnable tasks are alw a ys executed
sim ultaneously; they are spread out b y definition. F or the former, most ap-
plication actually do not require a sp ecific task to CPU placemen t, but only a
placemen t of tasks relativ e to eac h other. T ak e a VM as an example, to whic h
the host’s SMT capabilit y has b een passed through. F or a bunc h of SMT sib-
lings it do es not matter on whic h pro cessor core they are executed, as long as
30 CHAPTER 2. WHY COSCHEDULING?
all siblings are on the same core. This is something that a cosc heduler m ust
supp ort an yw a y in order to realize man y of the other cosc heduling use cases.
Not mapping tasks explicitly at application lev el giv es the sc heduler more
freedom to sh uffle things around for optimal p erformance. This use case is
explored later in Chapter 11.
2.3.2 Securit y
The guaran tee of sim ultaneous execution (or non-simultaneous execution, de-
p ending on the p oin t of view) pro vided b y cosc heduling can b e used to th w art
a subset of side-c hannel attac ks.
Side-Channel Elimination
Side-c hannels p ose a risk on m ultiprogrammed systems running un trusted co de,
as said co de ma y attempt to extract secrets b y in telligen tly monitoring the sys-
tem and its running applications. Ge et al. [47] surv ey v arious attac k metho ds
and coun termeasures.
With the in tro duction of m ultipro cessing, malicious co de is no longer limited
to time-sliced execution with victim co de: it is no w able to run sim ultaneously
with victim co de – enabling new metho ds of attac k. The effectiv eness of suc h
attac ks dep ends on differen t factors; the rule of th um b is, that the more resources
are shared b et w een CPUs, the more fine-grained observ ations can b e made,
increasing the c hance of finding a successful attac k. Coun termeasures are as
v aried as the attac ks themselv es. F or example, a t ypical coun termeasure on
the application-side is to ensure that the observ able b eha vior do es not differ for
v aried inputs. A t system lev el, one p ossibilit y is to mak e accurate observ ations
harder b y injecting artificial noise; another is to disable SMT, depriving an
attac k er of one of the more accurate observ ation options (see, e. g., [48] for an
approac h that com bines asp ects of differen t coun termeasures).
With cosc heduling, it is p ossible to dynamically disallo w sim ultaneous exe-
cution of un trusted co de with sensitiv e co de. Hence, sensitiv e co de can run in
isolation – on a core, on a pro cessor, or within the system, dep ending on the
use case – without the p ermanen t loss of parallel computations, reducing the
a v ailable side-c hannels.
A concrete example is the mitigation of the VM-based v arian t of the F ore-
shado w/L1 terminal fault (L1TF) vulnerabilit y on SMT-enabled In tel systems:
if the host utilizes extended page tables (EPT s), a malicious VM can read an y
data that is curren tly held within the L1 cac he [49, 50]. If the user do es not trust
their VMs, they ha v e basically t w o mitigation options: Either disable the use
of EPT s and fall bac k to shado w paging, taking a sev ere p erformance hit esp e-
cially when mitigations against Meltdo wn are in place within the VM [51, 52].
Or ensure that the L1 cac he is free of secrets, which is ac hiev ed b y a com bi-
nation of L1 cac he flushing when en tering a VM and ensuring that the other
2.3. SYSTEM DESIGN 31
SMT-sibling cannot execute an ything, that migh t load sensitiv e data in to the
cac he. Cosc heduling at core-lev el of tasks executing guest-co de of the same VM
ac hiev es this ob jectiv e without ha ving to disable SMT.
2.3.3 Device Managemen t
One function of an op erating system is the managemen t of devices. Usually ,
the op erating system pro vides access to ph ysical devices to applications only
through some la y ers of abstraction, e. g., files for storage on a disk, so ck ets for
comm unication o v er net w ork cards, or virtual devices instead of real ones when
op erating as a h yp ervisor. This typically requires request queues and buffers
within the op erating system to successfully share suc h devices b et w een m ultiple
applications.
These abstraction la y ers increase the memory fo otprint of the op erating sys-
tem and also induce some computational o v erhead. And ev en then, it is imp os-
sible in a m ultiprogrammed system to use some of the more sp ecialized function-
alit y at application lev el, such as dynamic v oltage/frequency scaling or net w orks
for collectiv e op erations. Hence, there are some attempts to reduce this o v er-
head again and to mak e sp ecial functions a v ailable to applications – particularly
within the virtualization and HPC areas.
Exclusiv e Device A ccess
The basic idea is to reduce the n um b er of applications that use a particular device
to one, i. e., to artificially “unshare” a device. With hardw are supp ort, this can
b e done b y mo ving the abstraction la y er in to the hardw are itself and pro vide
m ultiple views of the same device (e. g., via single ro ot I/O virtualization (SR-
IO V) as demonstrated in, e. g., [53]). This basically results in one exclusiv ely
o wned device p er application. Without hardw are supp ort (or if the hardw are
cannot pro vide enough virtual devices), cosc heduling can b e used to reduce the
n um b er of sim ultaneously executing applications, so that at most one application
accesses a particular device at a time. The device state is then included in ev ery
con text switc h of the application. Both v arian ts are depicted in Fig. 2.9. Due to
their differen t realization, b oth v arian ts ha v e their o wn use cases whic h o v erlap
only partially .
F or the cosc heduling v arian t, the device in question m ust b e free from ex-
ternal influences as it is practically non-existen t when the application is not
running. Originally , this w as used to optimize the comm unication la y er within a
cluster with m ultiple parallel applications [54, 55], where applications are allo w ed
to directly access device buffers and use adv anced functions of the connection
net w ork. This will b ecome interesting again with the up coming man ycore pro ces-
sors and their fast, sp ecialized on-c hip comm unication net w orks (e. g., [56, 57]).
Switc hing device state can b e more or less costly , whic h has to b e included
in the cost calculation. F or comm unication devices, for example, messages that
are in transit during a con text switc h require sp ecial atten tion.
32 CHAPTER 2. WHY COSCHEDULING?
C P U 1
C P U 2
v dev 1
v dev 2
A only
B only
A 1 B 1 A 1 B 1 A 1 B 1
B 2 A 2 B 2 A 2 B 2 A 2
(a) Exclusiv e device access via hardw are I/O virtualization. The same device presen ts
m ultiple views of itself. Each view is assigned exclusiv ely to an application.
C P U 1
C P U 2
dev ice
A 1
A 2
A only
A 1
A 2
A only
B 1
B 2
B only
B 1
B 2
B only
(b) Exclusiv e device access via cosc heduling. An application can access the device
whenev er it w an ts, cosc heduling ensures that at most one application can access a
device at a time.
Figure 2.9: T w o differen t tec hniques to “unshare” a normally shared device in a
m ulticore en vironmen t.
Exp ose System F unctionalit y
The op erating system cannot giv e applications con trol o v er functionalit y , that
directly influences the p erformance of the system or migh t ev en compromise its
in tegrit y , as this w ould create a conflict of in terests b et ween m ultiple applica-
tions. Ho w ev er, if coscheduling is used to mak e sure that tasks from the same
application are executed on all affected CPUs sim ultaneously , it is p ossible to
exp ose (mo derated) con trol o v er suc h functionalit y to applications.
F or example, con trol o v er dynamic v oltage/frequency scaling (D VFS), cer-
tain cac he and memory con troller settings, or p er-core/p er-pac kage p erformance
coun ters can b e exp osed to applications. Similar to the previous section, their
settings and curren t v alues ha v e to b e included in a con text switc h. This giv es
applications the abilit y to steer their part of the system to w ards their o wn opti-
mization goal – at times when they activ ely use the system. Examples include
a pro-activ e D VFS con trol instead of the t ypically reactiv e approac h tak en b y
op erating systems [58], and – in case of VMs – it allo ws mo ving the con trol lo op
from the host in to the guest, whic h can mak e more informed decisions ab out the
desired energy/p erformance trade-off for its w orkload.
Sp ecial Arc hitectures
F rom a hardw are design p oin t of view, cosc heduling also op ens the p ossibilit y to
ph ysically share devices whic h are t ypically realized p er-CPU, suc h as the mem-
ory managemen t unit or a virtually tagged cac he. Cosc heduling can guaran tee
that only tasks from the same application will access said device at an y time.
2.4. CHAPTER NOTES 33
This w ould create a middle ground b et w een full-fledged SMP systems and single
instruction/single data (SIMD) mac hines: while still able to execute m ultiple
instructions on m ultiple data elemen ts at an y one p oin t, these would need to
b elong to the same address space. A dditionally , other hardw are concepts suc h
as partner cores [59] can b e supp orted b y a general purp ose op erating system.
2.4 Chapter Notes
The use cases presen ted in this c hapter will b e analyzed from a cosc heduling p er-
sp ectiv e in Chapter 3: what are their particular requirements from a coscheduler,
and ho w can a single sc heduler supp ort them all. Note, how ev er, that while this
thesis considers all describ ed use cases at the theoretical lev el, it will not ac-
tually realize ev ery single use case. This has for most cases already b een done
b y other p eople as made eviden t b y v arious references and do es not ha v e to b e
rep eated. That said, some sp ecific use cases will b e co v ered in more detail in
later c hapters: Chapters 9 and 11 deal with system design, while Chapter 10
co v ers energy con ten tion and also touc hes the topic of exp osing shared frequency
con trols to applications.
The problem of energy con ten tion w as first published b y m yself in [60]. As
far as I kno w, I am the first with this thesis to suggest cosc heduling as a mean
to impro v e resp onse times of in teractiv e applications and throughput in p o w er-
capp ed scenarios, or as an alternativ e mean to realize concurrency and affinit y
managemen t.
34 CHAPTER 2. WHY COSCHEDULING?
Chapter 3
Cosc heduling Mo del
Since their in tro duction in 1982 and 1990, the concepts c osche duling [1] and
gang sche duling [43] ha v e b een used b y v arious authors. Due to new use cases
and newly iden tified asp ects, ho w ev er, man y differen t in terpretations of these
concepts w ere created and their original meaning has b een – while not lost –
somewhat diluted. A dditional confusion w as created due to c osche duling some-
times b eing used as in the original definition from Ousterhout [1], and sometimes
b eing used as it is realized (a bit differen tly) b y the algorithms presen ted in the
v ery same publication.
T o da y , cosc heduling and gang sc heduling are usually used as um brella terms
for tec hniques, that ac hiev e some kind of sim ultaneous execution of certain tasks.
In their original meaning, b oth are not v ersatile enough to co v er ev ery asp ect
that arises to da y . And while man y tec hniques ha v e b een dev elop ed that realize
some kind of cosc heduling, there is still no terminology to adequately express
cosc heduler capabilities or requiremen ts of use cases. T o address this, a mo del
for cosc heduling is dev elop ed in this c hapter. This mo del is able to capture the
essence of cosc heduling related asp ects of a sc heduler or a use case, whic h – for
instance – allo ws to easily select an adequate sc heduler for a certain use case or
a set of use cases.
The c hapter b egins in Section 3.1, where the common v o cabulary is estab-
lished that is used in this thesis. Section 3.2 collects sev eral cosc heduling-related
definitions and terms, that influenced the nomenclature used within the mo del.
Then, the mo del itself is constructed by first analyzing cosc heduling use cases
and their requiremen ts with resp ect to co ordination in time (Section 3.3) and
co ordination in space (Section 3.4). These insigh ts are then com bined in to the
desired mo del in Section 3.5. Finally , Section 3.6 examines differen t lev els of
adherence to the mo del and their consequences for cosc hedulers and use cases.
3.1 Basic T erms
In the literature, terms lik e cosc heduling are not the only ones with v arying
meanings. Ev en more confusion can arise around m uc h simpler terms, suc h as
35
36 CHAPTER 3. COSCHEDULING MODEL
pro cess, thread, or pro cessor. Is a pro cess something that can b e executed,
or is it just a con tainer for threads? Is a pro cessor a piece of hardware that
is plugged on to a motherb oard, or is a pro cessor just able to execute a single
strand of instruction? As this list go es on and on, this section will clear p ossible
am biguities of basic terms used in this thesis.
3.1.1 Soft w are
On the soft w are side, the basic unit is a task : a single strand of instructions that
are to b e executed within their o wn con text. Con trary to the terms pro cess or
thread, the term task shall ha v e no ingrained notion of the extend of its con text.
Throughout this w ork, tasks will often b e executed sim ultaneously . There shall
b e no preconceptions ab out their relations. On the one end of the sp ectrum,
they can op erate closely within the same address space; on the other end, they
can b e completely unrelated.
A bunc h of tasks, that w ork to wards a specific goal and w ere dev elop ed
in conjunction, form an applic ation . The applications in this w ork are mostly
parallel in nature – though the degree of team w ork will v ary . Sometimes it also
mak es sense to consider certain unrelated tasks as a unit. As a generic term for
an y sp ecific conglomeration of tasks, task gr oup will b e used.
The term sche duling entity (SE) will b e used for something that is sc heduled
b y the OS sc heduler. As suc h, ev ery task is also a sc heduling en tit y . Ho w ev er,
tasks are not the only SEs in this thesis: a group of SEs may b e represen ted b y
a sc heduling en tit y of its o wn. F rom the p oint of view of the OS sc heduler, a
sc heduling en tit y is in one of three p ossible states: running, ready , or blo c k ed.
F or a task, these states are defined as usual:
R unning (task): The task is curren tly b eing executed on a CPU.
Ready (task): The task is ready to b e executed, i. e., it is curren tly
w aiting for a CPU in the sc heduler’s runqueue.
Blo c k ed (task): The task cannot b e executed, curren tly . (Usually the task
is w aiting for something.)
While op erating systems often distinguish further states, these three are
univ ersally existen t and a further distinction is not necessary for this thesis.
F or a sc heduling en tit y that represen ts a group of SEs, the states ha v e to b e
defined differen tly , though the essence is preserv ed:
R unning (group): The state of at least one represen ted SE is running.
Ready (group): No SE is running, but a transition to running is p ossible.
Blo c k ed (group): The group is neither running nor ready .
If a SE is either ready or running, it is also referred to as runnable .
3.1. BASIC TERMS 37
System
- NUMA in terconnect
Pro cessor
- memory
- L3 cac he
CPU
CPU
CPU
CPU
CPU
CPU
Pro cessor
- memory
- L3 cac he
CPU
CPU
CPU
CPU
CPU
CPU
Pro cessor
- memory
- L3 cac he
CPU
CPU
CPU
CPU
CPU
CPU
Pro cessor
- memory
- L3 cac he
CPU
CPU
CPU
CPU
CPU
CPU
Figure 3.1: The system used in Chapter 9 represen ted as a tree of top ological
units. Ev ery CPU comes with its o wn set of execution units and an L1/L2 cac he.
3.1.2 Hardw are
One the hardw are side, m ulticore arc hitectures ha v e b ecome complex pieces of
tec hnology . In this thesis, a CPU is the basic unit in a m ulticore system; it is
able to execute just a single strand of instructions, i. e., a task. In a SMT system,
a pr o c essor c or e consists of m ultiple CPUs. Multiple pro cessor cores mak e up
a pr o c essor . Multiple pro cessors ma y o ccup y the same non-uniform memory
ac c ess (NUMA) domain , i. e., they hav e similar p erformance c haracteristics when
accessing memory . The whole system , finally , ma y consist of m ultiple NUMA
domains.
Most of to da y’s m ulticore arc hitectures can b e describ ed with this, and it
is fine when talking ab out actual systems. Conceptually , ho w ev er, this thesis
abstracts from these concrete hardw are comp onen ts and considers m ulticore
systems to b e comp osed of m ultiple top olo gic al units (TU s) . These TUs are
arranged in a tree according to the system top ology . The leaf TUs are CPUs,
while non-leaf TUs usually represen t in terconnection netw orks connecting their
c hildren. A dditionally , every TU ma y ha v e some resources asso ciated with it:
for example execution units, cac he or memory . The top ology of interconnection
net w orks themselv es do es not matter for most parts of the thesis. They can b e
bus-lik e, whic h is often the case within to da y’s pro cessors, or structured as the
in terconnection net w orks b et w een NUMA domains.
An example top ology tree is sho wn in Fig. 3.1, whic h sho ws one of the ev al-
uation systems that is used later on. The structure of this sp ecific top ology tree
is relativ ely simple. In a SMT system, CPUs and pro cessor cores would b e on
differen t lev els – with execution units attac hed to cores instead of CPUs. Also,
it is p ossible to adequately represen t, for instance, AMD’s Bulldozer microarc hi-
tecture [23] with execution units at CPU and at pro cessor core lev el. Complex
in terconnection net w orks, for example the NUMA interconnect in the SGI UV
arc hitecture [61], w ould b e similarly decomp osed and represen ted as m ultiple
lev els within the top ology tree.
38 CHAPTER 3. COSCHEDULING MODEL
3.1.3 P arallelism
When considering parallel applications or task groups in general, a c haracterizing
prop ert y from the p oin t of view of the OS is the n um b er of runnable tasks in
a group and whether that v aries o v er time. The n um b er of runnable tasks is
referred to as de gr e e of p ar al lelism (DOP) . Plotting the DOP o v er time results
in a p ar al lelism pr ofile for that task group. Dep ending on the system and its
load, it is usually not p ossible to alw a ys execute all runnable tasks. The n um b er
of running tasks is referred to de gr e e of c oncurr ency (DOC) . The DOC o v er time
results in a c oncurr ency pr ofile . That is, this thesis uses parallelism to refer to a
feature of an algorithm, while concurrency refers to the realization of parallelism
b y the op erating system.
F ollo wing [42], this thesis distinguishes four classes of task groups:
Rigid: A rigid task group has a fixed degree of parallelism.
Ev olving: An evolving task group c hanges its DOP during execution.
Moldable: A moldable task group is rigid, with the exception that the fixed
DOP is supplied b y the OS.
Malleable: A malleable task group is ev olving, with the exception that c hanges
in the DOP are triggered b y the OS.
So, in order for the OS to b e able to con trol the DOP of a task group, the task
group m ust b e malleable. The DOC, on the other hand, can alw a ys b e con trolled
b y the op erating system.
3.2 Cherry-pic king Cosc heduling T erms
This section describ es cosc heduling related terms and definitions that influenced
the cosc heduling mo del dev elop ed afterw ards. Ev ery concept includes a bit of
bac kground and ho w the original idea is reflected in the new cosc heduling mo del.
F or the sak e of simplicit y , a term is alw a ys attributed to the first author of the
publication, where it first app eared.
3.2.1 Ousterhout’s Cosc heduling
Ousterhout’s w ork [1] from 1982 defines the term c osche duling and presen ts three
algorithms to ac hiev e cosc heduling. It is regarded to da y as the k ey w ork in that
area. This and his earlier w ork coauthored with Scelza and Sindh u [62] together
form a summary of his Ph. D. thesis [63] of whic h a revised v ersion w as published
as a b o ok [64] in 1981. The thesis is ab out the design of a distributed op erating
system called Medusa.
Ousterhout’s cosc heduling definition rev olv es around groups of closely co op-
erating tasks, whic h he calls task for c es :
3.2. CHERR Y-PICKING COSCHEDULING TERMS 39
“A task force is cosc heduled if all of its runnable pro cesses are exe-
cuting sim ultaneously on differen t pro cessors. Each of the pro cesses
in that task force is also said to b e cosc heduled. ” [1]
T ask forces are considered to b e co op erating activities with fine-grained in terac-
tions based on lo c ks. The phenomenon of individual tasks rep eatedly blo c king
on lo c ks, b ecause of a not cosc heduled task force, is called pr o c ess thr ashing b y
Ousterhout.
Ousterhout also coined the term fr agmente d exe cution , whic h refers to cases
when at least one task of a task force is executed without b eing cosc heduled
according to the definition ab o v e. Another notable term is the Ousterhout matrix
as it is called b y other authors to da y . In one of his algorithms, Ousterhout used
a matrix to describ e the sc hedule. The matrix has one column p er CPU, while
the ro ws represen t differen t task slots. The idea is to fill this matrix with task
forces and ensure cosc heduling of ro ws of that matrix b y using a sync hronized
round-robin sc heduler. F or a more thorough description see Section 4.1.
This w ork uses the term cosc heduling in Ousterhout’s original meaning when
b eing formal: the mo del defines a cosc heduling constrain t. In less formal sit-
uations, cosc heduling is simply used as a synon ym for sim ultaneous execution.
A dditionally , this w ork uses the term gang , whic h is conceptually equiv alen t to
a cosc heduled task force.
3.2.2 F eitelson’s Gang Sc heduling
F eitelson and R udolph defined the term gang sche duling in their w ork [43] from
1990. F rom then on, b oth w ork ed together and also indep enden tly with others
on this topic on and off during the next 15 y ears. This resulted in a wide range of
publications ranging from mostly analytical w ork to more practical applications
of gang sc heduling. The initial w ork on that topic defined gang sc heduling as
follo ws:
“[Gang sc heduling is defined] as the sc heduling of a group of threads
to run on a set of pro cessors at the same time, on a one-to-one
basis. ” [43]
A later surv ey [65] added the requiremen t for time slices and collectiv e preemp-
tion to the definition of gang sc heduling, whic h w as only implied b eforehand.
The one notable difference of gang sc heduling to Ousterhout’s cosc heduling
is that tasks do not relinquish CPUs during their time slice. Even in a blo ck ed
state, a task still o ccupies its CPU as long as its group is executed. In suc h
a case its corresp onding CPU will b e idle. Ousterhout’s cosc heduling is less
strict and principally allo ws a CPU to pic k up some other task, while its actual
task is blo c k ed. Th us, gang sc heduling prev ents the cac he from b eing p olluted
and promotes the idea of actually o wning the hardw are while a task group is
executed.
40 CHAPTER 3. COSCHEDULING MODEL
This w ork will use the term gang roughly in this original meaning. Though,
CPUs are allo w ed to pic k up other w ork. A v arian t of a gang, whic h ac hiev es
the original meaning (and a bit more), is an isolate d gang in the terminology of
the mo del.
3.2.3 Arpaci-Dusseau’s Implicit Cosc heduling
Arpaci-Dusseau defined a cosc heduling mec hanism for clusters in 2001 that
cosc hedules tasks not based on explicitly sp ecified groups of tasks, but based
on implicit information. This implicit c osche duling [66] is realized b y an es-
p ecially crafted lo cal sc heduling p olicy and an adaptive w aiting sc heme that
monitors comm unication. Sp ecifically , the describ ed mec hanism relies on tasks
b eing w ok en on message arriv al and their utilization of spin-blo c king, where the
w aiting time is determined dynamically based on the ideal round-trip time and
arriv al rate of incoming messages. This w a y and together with a prop ortional-
share sc heduler, comm unicating tasks are automatically cosc heduled un til their
time slices are used up.
This w ork will use implicit c osche duling as a broader term for a family of
tec hniques that ac hiev e a desirable cosc heduling without activ ely selecting tasks
to b e cosc heduled. The opp osite of implicit cosc heduling will b e called explicit
c osche duling . These implicit tec hniques com bine sev eral seemingly unrelated
metho ds, whic h realize the desired cosc heduling as emergen t b eha vior.
F or example, another implicit tec hnique w as suggested b y Merk el et al.. This
Sorted Cosc heduling [67] fo cuses on the reduction of resource con ten tion. This is
done b y distributing tasks with equal resource demands ev enly across CPUs and
then using a sp ecial sc heduling p olicy that executes tasks on a CPU in order of
their exp ected resource demand. By alternating the execution order within ev ery
pair of CPUs and selecting appropriate time-slice lengths, tasks with con trary
demands are cosc heduled.
3.2.4 VMw are’s Relaxed Cosc heduling
The sc heduler in VMw are’s vSphere Hyp ervisor [9, 68] is capable of a form of
cosc heduling. Con trary to the cosc heduling v arian t implemen ted in their earlier
pro duct, the curren t v ersion supp orts what they call r elaxe d c osche duling . This
relaxed cosc heduling do es not enforce sim ultaneous dispatc hing and preemption
as F eitelson’s gang sc heduling. Th us, a fragmen ted execution is principally al-
lo w ed. The p ossibly negativ e effects of fragmen ted execution are con tained b y
limiting the allo w ed skew , i. e., b y limiting the difference in vCPU progress.
This thesis uses the term r elaxe d in situations, when a certain sc heduling
guaran tee – suc h as sim ultaneous execution or a certain placemen t – is not
enforced (or required) all the time. Instead, some (minor) deviations from a
guaran tee are allo w ed, whic h giv es a cosc heduler implemen tation more freedom.
If applications do not require a strict adherence to a guaran tee, they are exp ected
to handle suc h a deviation somewhat gracefully .
3.3. ASPECTS OF TIME 41
t 1
t 2
(a) Classic t w o-sided cosc heduling. Whenev er at least one task of a group of runnable
tasks is running, all tasks of said group are running.
t 1
t 2
(b) One-sided cosc heduling. A task ma y only b e executed, when a certain other task
is running, but it do es not ha v e to.
t 1
t 2
(c) An ti-cosc heduling. A task ma y not b e executed, when a certain other task is
curren tly executed.
Figure 3.2: Differen t in terpretations of cosc heduling. All three v arian ts ha v e
merit.
3.3 Asp ects of Time
Cosc heduling is – in essence – the con trolled simultaneous execution of tasks in
a parallel system. That is, a cosc heduler activ ely con trols whic h subset of tasks
is running at an y p oin t in time. This con trol can come in t w o fla v ors: tasks
can b e forced to execute sim ultaneously , or their sim ultaneous execution can b e
prev en ted. T raditionally , cosc heduling is ab out the former v arian t: exploiting
synergies of sync hronous execution. But esp ecially with resource con ten tion in
mind, the second v arian t can also b e attractiv e: a v oiding sim ultaneous execution
of tasks that stress the same resources.
A particular p oin t of sim ultaneous execution is, that it do es not ha v e to b e
symmetrical as describ ed b y Ousterhout [1] and F eitelson [43]. Just b ecause a
task A should alw a ys b e executed while task B is running, it do es not mean
that A m ust alw a ys b e executed when B is running. All three in terpretations of
cosc heduling are depicted in Fig. 3.2. This leads to a definition of cosc heduling,
that is sligh tly differen t from all those definitions and in terpretations previously
published.
Definition 1 (Cosc heduling) . A task is said to b e cosc heduled with another
task, when it is only executed when this other task is also curren tly executed
(cf. Fig. 3.2b).
This asymmetric definition allo ws to co v er more use cases, sp ecifically cer-
tain t yp es of auxiliary tasks and an adaptiv e solution to handle lo c k holder
42 CHAPTER 3. COSCHEDULING MODEL
t 1
t 2
Figure 3.3: Not cosc heduling. Enforcing an execution order of tasks – here t 2 is
alw a ys executed directly after t 1 – is not in the scop e of cosc heduling.
preemption (b oth co v ered later in Chapter 11). What even this extended defi-
nition of cosc heduling do es not – and should not – co v er, is the execution order
of tasks. That is, it is imp ossible to sp ecify constraints lik e “task A is alw a ys
executed directly after task B ” (cf. Fig. 3.3). While it w ould mak e sense in
some cases to use suc h a constrain t (e. g., b ecause tasks A and B share data and
doing so w ould increase the cac he hit rate), this addresses a problem that is also
presen t on unipro cessor systems. And this thesis considers cosc heduling to b e a
m ultipro cessing problem, that strictly addresses the topic of sim ultaneousness.
When not considering just t w o tasks, but p otentially larger groups of tasks,
only the traditional symmetric cosc heduling b eha vior mak es sense: all tasks
should b e executed sim ultaneously . Ho w ev er, with groups of tasks it is no w
necessary to sp ecify group b eha vior when not all tasks within the group are
runnable. Both, Ousterhout and F eitelson, agree that cosc heduled tasks should
b e able to blo c k individually instead of one blo c king task causing the whole
group to get blo c k ed. This leads to the follo wing definition of a gang.
Definition 2 (Gang) . A task group is said to b e a gang, when all runnable tasks
are cosc heduled with eac h other, i. e., all runnable tasks are alw a ys executed
sim ultaneously (cf. Fig. 3.2a).
This means in particular, that a gang is alw a ys able to realize its full parallel
p oten tial and that its concurrency profile is iden tical to its parallelism profile.
Ho w ev er, neither Ousterhout nor F eitelson (nor an y one else) giv es a definition for
cosc heduled groups of tasks, that can b e used throughout all use cases. In par-
ticular, it is necessary to break the coupling of the n um b er of cosc heduled tasks
to the n um b er of runnable tasks in a group for man y of the m ulticore-sp ecific use
cases. Remo ving this coupling yields a v ery general definition, whic h basically
sa ys that sim ultaneous execution is done on purp ose and not b y acciden t.
Definition 3 (Sc heduled task group) . A task group is said to b e sc heduled,
when the degree of concurrency of the task group is activ ely con trolled b y the
sc heduler.
This definition in itself is to o broad to b e of m uc h use. F or that, the con trol
exerted b y the sc heduler needs to b e defined. This is done in the form of freely
com binable constrain ts that describ e the desired concurrency profile in more de-
tail. T o regain the b eha vior of a gang as defined ab o v e, the degree of concurrency
needs to b e maximized, basically rein tro ducing the just brok en decoupling.
3.3. ASPECTS OF TIME 43
time
parallelism
minimal concurrency
maximal concurrency
Figure 3.4: Effects of concurrency constrain ts on a sc heduled task group. The
sc heduler can freely select the degree of concurrency within grey area.
Definition 4 (Cosc heduling constrain t) . The degree of concurrency within a
task group is alw a ys equal to the n um b er of runnable tasks, whenev er a task is
executed.
This constrain t is needed mainly for situations that include activ e w aiting
or other forms of sync hronization, where a sync hronous execution of tasks is
indicated.
In situations, where sim ultaneous execution of all runnable tasks is not
strictly required, it ma y still b e necessary to restrict the degree of concurrency ,
so that it either do es not go ab o v e a certain lev el or b elo w. This – in its pure
form – is mostly needed for concurrency con trol use cases. A dditionally , it is
implicitly included in man y of the placemen ts constrain ts that are discussed in
the next section.
Definition 5 (Minimal concurrency constrain t) . The degree of concurrency
within a sc heduled task group nev er go es b elo w a certain v alue, while it is ex-
ecuted and enough runnable tasks are a v ailable. When there are not enough
runnable tasks, the DOC is k ept equal to the DOP , whenev er a task is executed.
Definition 6 (Maximal concurrency constrain t) . The degree of concurrency
within a sc heduled task group nev er go es ab o v e a certain v alue. If more runnable
tasks are a v ailable, care is tak en to alw a ys execute only a subset of the a v ailable
tasks sim ultaneously .
These constrain ts giv es the sc heduler a bit more freedom than the cosc hedul-
ing constrain t. Their effects are illustrated in Fig. 3.4, whic h sho ws a parallelism
profile with allo w ed degrees of concurrency shown in grey . Please note, that a
reduction of concurrency w ould result in a prolonged execution in realit y .
With the minimal concurrency constrain t, a group is still sc heduled when
there are not enough runnable tasks to reac h the minimal degree of concurrency .
The option to cease execution of a task group, when the n um b er of runnable
tasks drops b elo w a limit, also exists.
44 CHAPTER 3. COSCHEDULING MODEL
Definition 7 (Minimal parallelism constrain t) . If the n um b er of runnable tasks
within a sc heduled task group go es b elo w a certain v alue, no task of the group
is executed.
This constrain t is mainly needed for applications with hard demands on
sync hronous execution so that – for instance – in the ev en t of one task running
in to a page fault, the others stop executing immediately . Ho w ev er, it m ust b e
applied carefully in order not to acciden tally stop execution of a group, where
only one of the remaining runnable tasks w ould create or un blo c k other tasks
within the same group. Usually , this constrain t has to b e driv en dynamically
to accoun t for v olun tary blo c king and an allo w ed sk ew in progress caused b y
in v olun tary blo c king. A constraint to cease execution when there are to o m uc h
runnable tasks do es not mak e an y sense (except ma yb e to detect erroneous
application b eha vior), as runnable tasks usually do not disapp ear without b eing
executed.
3.4 Asp ects of Space
When a sc heduled task group do es not span the whole system, the question of
wher e within the system it should b e executed m ust b e answ ered. Here, eac h
sc heduled task group ma y ha v e its o wn idea of a supp osedly go o d mapping.
T raditionally , a task group w as equiv alen t to a parallel application. And es-
p ecially on clusters the b est practice usually is: execute tasks of a task group
close together, so that communication dela ys across the in terconnection net w ork
are minimized. With to da y’s NUMA systems and also cac he arc hitectures, this
st yle of placemen t is also relev an t for individual computer systems. This is again
expressed as a constrain t that can b e attac hed to a sc heduled task group.
Definition 8 (Close placemen t constrain t) . A task group is mapp ed to a top o-
logical unit as deep as p ossible within the top ology tree, that can still accom-
mo date the DOC of the task group. Within the selected top ological unit, CPUs
are selected so that distances are minimized.
This constrain t causes a task group to b e mapp ed to a set of CPUs that share
as man y resources as p ossible, whic h should pro vide reasonably lo w comm uni-
cation costs. The supp ort of a v ariable DOC mak es this constrain t particularly
useful for parallel applications, where the degree of parallelism is not tied to
certain arc hitectural prop erties. P ossible placemen ts of task groups of differen t
degrees of concurrency are exemplified in Fig. 3.5.
Use cases that address resource con ten tion or con tain arc hitecture-sp ecific
optimizations, need more sp ecific constrain ts. Namely , that a certain resource
is shared b et w een CPUs used b y a task group – or the direct opp osite, that a
certain resource is explicitly not shared. P ossible resources are: execution units,
differen t lev els of cache, memory , comm unication net w orks, etc. Additionally ,
3.4. ASPECTS OF SP ACE 45
System
Pro cessor Pro cessor Processor Pro cessor
T ask group with
close placemen t
( D O C > 6 )
T ask group with
close placemen t
( 2 ≤ D O C ≤ 6 )
T ask group with
close placemen t
( D O C = 1 )
T ask group with
L3 sharing
T ask group with
L3 non-sharing
Figure 3.5: Pic king up the top ology tree from Fig. 3.1, this sho ws p ossible
mappings of task groups with certain placemen t constrain ts within the system.
there are less tangible resources lik e frequency con trols, shared p o w er budgets,
co oling capacities, and p ossibly more.
Definition 9 (Resource sharing constrain t) . All tasks of a task group are
mapp ed to a top ological unit (or b elow it), to whic h the resource in question is
attac hed.
Definition 10 (Resoure non-sharing constrain t) . T asks of a task group are
mapp ed to a most one CPU within eac h top ological unit that has its o wn instance
of the resource in question.
Both constrain ts limit the n um b er of CPUs allo catable to a task group. Th us,
there is an implicit maximal concurrency constrain t. In the example giv en in
Fig. 3.5, the DOC ma y b e at most six, when L3 cac he should b e shared, and
four when eac h task should b e able to access a differen t L3 cac he, so that there
is no in terference of other tasks of the same group. These constrain ts differ from
the close placemen t constrain t, in that they are tied to top ological units at a
certain lev el of the top ology tree, while the close placemen t constrain t adapts to
the task group p ossibly encompassing the whole system.
Some task groups are able to fully utilize resources of a top ological unit, but
without using all of the CPUs. Suc h cases need an additional guaran tee that
in terference within the top ological unit b y tasks of other groups will not happ en.
Definition 11 (Isolation constrain t) . When a sc heduled task group is executed,
no other tasks are executed sim ultaneously that migh t in terfere b y accessing the
isolated resource.
A cosc heduled, isolated task group resem bles F eitelson’s definition of a gang,
that k eeps un used CPUs idle. The only difference is that the n um b er of CPUs
is primarily determined b y the hardw are instead of the o v erall n umber of tasks
46 CHAPTER 3. COSCHEDULING MODEL
within a task group. Still, b oth decouple the n um b er of assigned CPUs from the
n um b er of running tasks.
Finally , it is p ossible to define the opp osite of the close placemen t constrain t.
Though, the goal is not to maximize comm unication costs, rather tasks should
share as few resources with eac h other as p ossible.
Definition 12 (Indep enden t placemen t constrain t) . T asks within a task group
are spread throughout the system, so that the p oten tially a v ailable resources are
maximized.
In some sense, this is a v arian t of the non-sharing constrain t, that do es
not sp ecify an explicit resource. Th us, it is able to grow and shrink with the
concurrency of its task group, similar to the close placemen t constrain t. This
constrain t basically resem bles the default sc heduling p olicy of most op erating
system sc hedulers that ha v e an understanding of to da y’s m ulticore arc hitectures:
using first one CPU p er pro cessor b efore utilizing the others – and then using
just one CPU p er core, while enough CPUs are a v ailable.
3.5 Cosc heduled Sets
With a sc heduled task group and attac hed constrain ts, it is no w p ossible to de-
scrib e the sc heduling requiremen ts of most use cases of Chapter 2 adequately .
But esp ecially for nested use cases, the concept is not y et flexible enough. While
the close and indep enden t placemen t constrain ts w ork with arbitrarily degrees
of concurrency , they lac k the more fine-grained expressiv eness of the resource-
sp ecific constrain ts. These, on the other hand, imp ose concurrency limits on
sc heduled task groups. It is, for instance, imp ossible to define a sc heduled task
group for a VM with a passed through system arc hitecture; or to express nested
parallelism, where the outer lev el utilizes NUMA domains and the inner lev el
CPUs of a m ulticore pro cessor. Also, it is imp ossible for an op erating system
to optimize the mapping of a parallel application that already requested to b e
cosc heduled. T o facilitate these and other scenarios, sc heduled task groups are
generalized in to c osche dule d sets , whic h ac hiev e the desired level of expressiv e-
ness.
Definition 13 (Cosc heduled Set) . A cosc heduled set is the com bination of a
group of one or more sc heduling en tities and a set of sc heduling constrain ts. A
cosc heduled set is a sc heduling en tit y itself. A nested cosc heduled set ma y not
con tain conflicting constrain ts.
Sc heduling constrain ts on groups of SEs are defined similarly to the con-
strain ts on sc heduled task groups kno wn from the previous t w o sections. These
constrain ts just op erate on SEs instead of tasks, and the definitions for DOC
and DOP are based on the n um b er of running or runnable SEs, resp ectiv ely .
3.5. COSCHEDULED SETS 47
System
indep enden t placemen t
A pplic ation
cosc heduling,
close placemen t
A pplic ation
cosc heduling,
close placemen t
V irtual machine
cosc heduling,
L3 sharing
V irtual c or e
cosc heduling,
ex. unit sharing
V irtual c or e
cosc heduling,
ex. unit sharing
Figure 3.6: Exemplary setup of cosc heduled sets for differen t kinds of parallel
applications: t w o simple parallel applications and a m ulticore VM that is able
to effectiv ely utilize SMT.
With these definitions in place, it is no w p ossible to describ e all use cases
presen ted in Chapter 2. Use cases in the area of application and system design
usually ha v e a straigh t-forw ard translation in to cosc heduled sets: F or eac h par-
allel application, a (p ossibly nested) cosc heduled set is created. This set then
often exists for the lifetime of the application. Dep ending on the structure of a
program, cosc heduled sets can also b e created or mo dified on demand for certain
execution phases.
F or example, a simple parallel application could b e describ ed b y a cosc hed-
uled set with a cosc heduling constrain t and a close placemen t constrain t. A
system is able to handle m ultiple of these applications b y adding their sets to
a cosc heduled set with an indep enden t placemen t constrain t. A more complex
parallel application, for instance a VM whic h tak es adv an tage of the m ulticore
and SMT arc hitecture of its host, w ould consist of m ultiple lev els in itself: SMT
siblings should b e executed sim ultaneously and they should b e placed within the
same core of the host. Thus, for eac h core within the VM there is a cosc hed-
uled set with a cosc heduling constrain t and an execution unit resource sharing
constrain t in whic h an appropriate amount of vCPUs is placed. (An optional
execution unit isolation constrain t k eeps p erformance within a partly loaded VM
similar to a non-virtualized at the cost of idle computational resources in the
host.) These cosc heduled sets, in turn, are placed in a cosc heduled set with
a cosc heduling constrain t and an L3 cache resource sharing constrain t, so that
the cores of the VM are executed sim ultaneously and placed within the same
pro cessor within the host. Both t yp es of parallel applications are illustrated in
Fig. 3.6. Of course, man y differen t setups are p ossible to supp ort differen t use
cases.
F or resource managemen t use cases, whic h mostly consider b eha vior of in-
dividual tasks in order to executing go o d com binations of them sim ultaneously ,
the translation in to cosc heduled sets is less straigh t-forw ard. Dep ending on the
48 CHAPTER 3. COSCHEDULING MODEL
System
indep enden t placemen t
Sele cte d tasks
cosc heduling,
resource sharing
Sele cte d tasks
cosc heduling,
resource sharing
(a) Indep enden t execution of p ossibly
man y cosc heduled sets with sp ecifically
tailored go o d task com binations.
System
cosc heduling
T ask class
ind. placemen t
max. concurrency
T ask class
ind. placemen t
max. concurrency
(b) Cosc heduling of a few task classes.
An y p ossibly resulting com bination of
tasks is go o d.
Figure 3.7: Differen t w a ys to handle resource managemen t use cases.
actual use case and the executed applications, b eha vior of individual tasks ma y
c hange more or less often – in addition to dynamic creation and termination
of tasks. Basically , there exist t w o w a ys to deriv e cosc heduled sets for these
scenarios. The first p ossibilit y is to organize tasks in to m ultiple flat cosc hed-
uled sets, with eac h set b eing optimized for the considered resource. Th us, eac h
cosc heduled set has a cosc heduling constraint and a resource sharing constrain t.
These sets are all c hildren of a set with just an indep enden t placemen t constrain t.
There is no need to force sim ultaneous execution at that lev el. This is illustrated
in Fig. 3.7a. The alternativ e is to create classes of tasks with roughly iden tical
b eha vior regarding a resource, and then to execute classes sim ultaneously that
complemen t eac h other. Th us, there is a cosc heduled set for eac h class with
either a resource non-sharing constrain t or an indep enden t placemen t constrain t
with an explicit maximal concurrency constrain t. These sets are com bined b y a
set with a cosc heduling constrain t. This case is illustrated in Fig. 3.7b.
Both of these approac hes ha v e their adv an tages and disadv an tages. The first
v arian t allo ws a more fine-grained con trol, whic h is useful when a classification
sc heme is unreasonable for some reason or resource usage can b e measured v ery
accurately . The second v arian t trades this con trol and accuracy for less syn-
c hronization and less o v erhead at run time. Of course, one can create arbitrary
solutions that la y somewhere b et w een these t w o. F or instance, one could ha v e
m ultiple sets of classes with eac h set yielding go o d com binations.
Ov erall, it is p ossible to construct a cosc heduled set for ev ery use case. T a-
ble 3.1 giv es an o v erview, whic h constraints w ould normally b e utilized for whic h
use case. Due to the mo dular nature of this cosc heduling mo del, it is easy to
ha v e sev eral use cases side b y side, suc h as parallel applications b eing cosc hed-
uled and normal tasks b eing sc heduled resource-a w are. The building blo c ks can
simply b e com bined in a cosc heduled set with an indep enden t placemen t con-
strain t. Similarly , nesting of use cases, suc h as resource-a w are placemen t of tasks
within a cosc heduled parallel application or resource-a w are placemen t of m ulti-
ple cosc heduled parallel applications, can b e expressed equally simple b y nesting
the building blo c ks.
3.5. COSCHEDULED SETS 49
T able 3.1: Use cases and their t ypically required constrain ts. If not all instan ti-
ations of a use case t ypically require a constrain t, this is indicated b y brac k ets.
Not sho wn is the alternativ e realization of conten tion use cases describ ed in the
text, that is more similar to the optimization use cases.
Use Case
Cosc heduling
Minimal P arallelism
Minimal Concurrency
Maximal Concurrency
Close Placemen t
Indep enden t Placemen t
Resource Sharing
Resource Non-Sharing
Resource Isolation
A pplic ation Design
Fine-grained sync hronization ✓ ( ✓ ) ✓
Scalabilit y of algorithms ✓ ( ✓ ) ✓
Resp onse time ✓ ✓ ( ✓ )
Lo c k holder preemption ✓ ( ✓ )
Static load balancing ✓ ( ✓ )
A uxiliary tasks ✓ ( ✓ ) ( ✓ ) ( ✓ )
Execution unit optimizations ✓ ✓ ✓
Cac he optimizations ✓ ✓ ✓
R esour c e Management
Memory pressure ( ✓ ) ( ✓ ) ✓ ✓
Cac he pressure ( ✓ ) ( ✓ ) ✓ ✓
Execution unit con ten tion ( ✓ ) ( ✓ ) ( ✓ )
Cac he/memory bandw. con ten tion ( ✓ ) ( ✓ ) ( ✓ )
T emp erature balancing ( ✓ ) ( ✓ ) ( ✓ )
P o w er capping ( ✓ ) ( ✓ ) ( ✓ )
Energy con ten tion ✓ ✓
System Design
P arallel application managemen t ( ✓ ) ( ✓ ) ( ✓ ) ( ✓ ) ( ✓ ) ( ✓ ) ( ✓ ) ( ✓ ) ( ✓ )
Concurrency managemen t ( ✓ ) ( ✓ ) ( ✓ )
Affinit y managemen t ( ✓ ) ( ✓ ) ( ✓ )
Side-c hannel elimination ✓
Exclusiv e device access ✓ ✓
Exp ose system functionalit y ✓ ✓
Sp ecial arc hitectures ✓ ✓
50 CHAPTER 3. COSCHEDULING MODEL
3.6 Theory vs. Practice
The sc heduling constrain ts attac hed to cosc heduled sets carry certain guaran-
tees on whic h use cases rely . Unfortunately , it is unlik ely – if not do wnrigh t
imp ossible – for a cosc heduler to adhere to eac h and every constrain t b y the
letter. A cosc heduler migh t supp ort only a subset of constrain ts, constrain ts are
not realized exactly as describ ed, or constrain ts migh t get violated from time to
time. In some cases, this is of critical imp ortance for a use case; and sometimes
it w ould just ha v e b een nice for the extra p ercen t of p erformance. In particular,
the follo wing constrain t violations could happ en more or less often:
F ragmen ted execution: There are less SEs running sim ultaneously than
there should b e.
Ov erstretc hed execution: There are more SEs running sim ultaneously than
there should b e.
Misaligned execution: The SEs are running on a set of CPUs, whic h
do es not satisfy a placemen t constrain t.
Non-isolated execution: Other SEs are in terfering with running SEs al-
though this w as forbidden.
One violation that is almost imp ossible to a v oid completely is fragmen ted
execution. When the cosc heduler decides to execute a cosc heduled set, its tasks
m ust transition from ready to running. Unless there is a barrier of some kind
in v olv ed, this transition will happ en more or less gradually and migh t also in-
clude a short phase of non-isolated execution as depicted in Fig. 3.8a. Similar
reasoning can b e applied to blo c king tasks within a set with a minimal paral-
lelism constrain t, where this information needs to b e carried to other CPUs,
or handling of an in terrupt on a CPU (cf. Fig. 3.8b). In b oth cases, there
are short momen ts where constrain ts are violated. While nothing can b e done
ab out message latency , the effect of in terrupts can b e mitigated b y disabling or
sync hronizing their activities. But that is neither alw a ys p ossible nor alw a ys
necessary . (Note, that fragmen ted execution ma y also o ccur, when there are to o
man y SEs in a set with a cosc heduling constrain t. But as this usually p oin ts to
an issue with the use case and not the cosc heduler, it is ignored here.)
Without quan tifying it exactly , it is p ossible to distinguish three levels of
adherence to a constrain t, that can b e realized by a cosc heduler or demanded
b y a use case: relaxed, strict, and precise. The default in this thesis is a strict
adher enc e to constrain ts, where only (v ery) short term violations during con text
switc hes, in terrupts, or other reconfigurations are allo w ed. A pr e cise adher enc e
forbids ev en those, while under a r elaxe d adher enc e constrain ts migh t b e vio-
lated for longer p erio ds of time, i. e., the guaran tees are turned in to b est-effort
approac hes. The less exact the adherence, the less costly is usually its realization
and the more freedom for an implemen tation exists.
3.6. THEOR Y VS. PRACTICE 51
C P U 1
C P U 2
C P U 3
C P U 4
A 1
A 2
A 3
A 4
B 1
B 2
B 3
B 4
(a) Gradual con text switc h from A to B
or message latency when A transitions
to blo c k ed after A 2 blo c ks.
C P U 1
C P U 2
C P U 3
C P U 4
A 1
A 2
A 3
A 4
E
E E
E
(b) In terrupt handling.
Figure 3.8: T ypical sources of transien t fragmen ted execution.
C P U 1
C P U 2
C P U 3
C P U 4
A 1
A 2
A 3
A 4
A 1
A 2
A 3
A 4
B B
Figure 3.9: P oten tially unreasonable adherence to A ’s cosc heduling constrain t.
Ignoring it, w ould more than double the usable CPU time. Unless A exp eriences
a sev ere p erformance drop or energy consumption is imp ortan t, A should not b e
cosc heduled.
If a use case do es not get its desired guaran tees, p oten tial consequences range
from often more anno ying issues, suc h as an increased energy consumption, to
more noticeable problems – usually p erformance issues. P erformance-wise, the
w orst case is a standstill as seen with activ e w aiting or liv elo c ks: sp ending CPU
cycles without making an y progress. Certain use cases might ev en fail or pro duce
errors, when a constrain t is not fulfilled, suc h as an op erating system panic k-
ing when executed within a m ulticore VM b ecause of not prop erly sync hronized
vCPUs. Other p oten tial issues are securit y concerns: ma yb e an isolation con-
strain t is used to close certain side-c hannels and a violation of that constrain t
migh t allo w an attac k er to compromise the system.
In the end, the op erating system sc heduler has to trade constrain t adherences
on a case b y case basis against the o v erall sc heduling goal. F or instance a sligh tly
decreased p erformance or increased energy consumption of one application migh t
b e tolerable, when this decision remo v es fragmen tation, indirectly gaining more
profit o v erall. The example in Fig. 3.9 sho ws a system with a cosc heduled
parallel application and an application consisting of a single task. CPU time
is distributed fair, eac h application receiv es 50%. Violating the cosc heduling
constrain t w ould more than double the distributable CPU time. If one do es
not care ab out the increased energy consumption, this w ould b e a w orth while
trade-off, unless the p erformance drop of the formerly cosc heduled application
is sev ere.
52 CHAPTER 3. COSCHEDULING MODEL
3.7 Chapter Notes
The cosc heduling mo del presen ted in this c hapter is pick ed up in Chapter 4,
where the capabilities of sev eral existing cosc heduling approac hes are assessed,
and in Chapter 6, where an algorithmic framew ork is laid out, whic h realizes
this mo del. The question of ho w the op erating system can actually manage
an arbitrary agglomeration of cosc heduled sets fairly and efficien tly (including
necessary trade-offs) is considered later as part of Chapter 9. A sp ecific nested
use case, namely the resource-a w are placemen t of whole parallel applications, is
discussed later as part of Chapter 11.
With resp ect to application failures caused b y an absence of cosc heduling,
I p ersonally exp erienced Lin ux VMs failing to b o ot some secondary CPUs and
F reeBSD VMs panic king when the sk ew in progress of vCPUs b ecomes to o large
in the wrong momen t. Mind y ou, that w as during regular usage without actually
trying to force an ything.
Chapter 4
Cosc heduling Approac hes
This c hapter presen ts several existing cosc heduling approac hes. Each approac h
is analyzed and put in to p ersp ectiv e with resp ect to the definitions giv en in
Chapter 3. Sp ecifically , we lo ok at the supp orted sc heduling constrain ts and
what that means in terms of v ersatilit y .
This starts in Sections 4.1 and 4.2 with Ousterhout’s cosc heduling algorithms
and F eitelson’s gang sc heduling approac h, resp ectiv ely . These tw o ha v e basically
created a researc h area of its o wn. Section 4.3 presents another approac h that
w as created sp ecifically with clusters in mind. Then, more differen t from the
approac hes so far, the cosc heduler within VMw are ESXi is examined in Sec-
tion 4.4. Finally , in Section 4.5 some researc h on top of these approac hes is
briefly discussed.
Please note, that some additional sc heduling approac hes – which do not
exactly realize a form of cosc heduling – are discussed later as part of Chapters 9
and 10.
4.1 Ousterhout’s Cosc heduling
In his w ork ab out cosc heduling [1], Ousterhout defines three algorithms, that try
to ac hiev e cosc heduling according to his definition. While the design prop osed b y
this thesis differs greatly from Ousterhout’s approac h, his w ork can b e considered
as the start of the researc h in to cosc heduling.
The Matrix metho d uses, what is called b y other authors to da y , an Ousterhout
matrix . In this matrix CPUs are represen ted as columns and time-slices as ro ws.
While the n um b er of columns is fixed, the n um b er of ro ws can v ary dynamically
dep ending on the load in the system. A group of tasks is placed in the first ro w
that has enough free en tries to hold all tasks of that group – no matter whether
they are runnable or not. Sc heduling is then globally sync hronized, executing
the ro ws round-robin, one ro w at a time. Ev ery CPU executes the task from the
curren t ro w in its o wn column. In cases when said task is not runnable or blo c ks
during execution, the CPU cyclically scans its o wn column for an alternate task
to execute.
53
54 CHAPTER 4. COSCHEDULING APPR O A CHES
Applying the terminology from Chapter 3, the Matrix metho d supp orts only
flat cosc heduled sets without an y placemen t constrain ts. F rom the time con-
strain ts only the cosc heduling constrain t is supp orted – and that in a relaxed
manner due to the additional “out-of-order execution” of tasks when the original
task is not runnable.
The Continuous algorithm unra v els the matrix ro w-wise yielding a long arra y .
This arra y is then alw a ys accessed b y considering sliding windo ws with a size
equal to the original ro w length, that is, a windo w ma y span t w o ro ws within the
original matrix represen tation still with one en try p er CPU. A group of tasks is
placed within the arra y b y sliding a windo w, un til the windo w has enough free,
not necessarily consecutiv e elemen ts to hold the whole group. F or scheduling
tasks, a windo w is adv anced ev ery time-slice so that the leftmost elemen t in
the windo w is the leftmost elemen t of a group of tasks that has not yet been
cosc heduled with maxim um concurrency in the curren t pass through the arra y .
That windo w is then executed. F or elemen ts with no runnable task, the alternate
selection of the matrix metho d is used, letting a CPU scan through what w ould
b e its column in the original represen tation. The Undivide d algorithm is similar
to the Con tin uous algorithm with the difference that groups are only placed in
consecutiv e elemen ts within the windo w.
These t w o algorithms are iden tical to the Matrix metho d with resp ect to their
cosc heduling abilit y , though they are a bit less relaxed. Also, they distribute
CPU time more fairly . Dep ending on the mapping of CPUs to column indices
within the matrix, the Undivided algorithm can b e used to realize relaxed close
and indep enden t placemen t constrain ts.
4.2 Distributed Hierarc hical Con trol
Distributed Hierarc hical Con trol (DHC) is an approac h describ ed b y F eitelson
and R udolph [43] to ac hiev e cosc heduling. DHC shares a few similarities with
the cosc heduler design prop osed in this thesis, y et it differs in others. It w as pro-
p osed in 1990 targeting early parallel mac hines and w as later adapted to w ork on
clusters [69]. An adaptation to w ards m ulticore systems with their unique prop-
erties and to da y’s soft w are requiremen ts w as not done. Due to some similarities,
this thesis could b e considered as this adaptation.
Similar to the approac h prop osed in this thesis, DHC also uses a hierarc hical
structure in ternally , alb eit it is constructed differen tly . DHC’s structure is less
flexible and ignores the top ology of the system. It mainly considers scalabilit y of
the approac h itself, without considering the scalabilit y of the executed soft w are.
Initially , fairness w as barely considered, whic h w as addressed later [70]. Still,
the prop osed fairness is sk ew ed to w ards smaller gangs, and a fair distribution of
CPU time at application lev el – whic h is considered essen tial in this thesis – is
explicitly discarded. DHC do es not supp ort an y nesting and in teractivit y is not
considered. Also blo c king tasks and dynamic group sizes are ignored.
4.2. DISTRIBUTED HIERAR CHICAL CONTR OL 55
DHC builds a binary tree of con trollers on top of the CPUs of the system.
Eac h con troller is resp onsible for the controllers underneath and, th us, for a cer-
tain n um b er of CPUs. Eac h con troller is asso ciated with a runqueue, that holds
gangs whic h just fit this con troller but not a child con troller. Eac h con troller is
either activ e, disabled, or idle. An activ e con troller sc hedules its gangs activ ely
and can con trol the state of c hild con trollers. A disabled con troller simply do es
as it is told b y the paren t, i. e., it sc hedules a part of a gang somewhere ab o v e it
in the hierarc h y . An idle con troller w aits for the end of the curren t sc heduling
round. A new sc heduling round is started b y making the ro ot con troller activ e.
This con troller then disables all c hild con trollers and sc hedules eac h of its gangs
for a time slice. After that, it mak es its c hildren activ e and b ecomes idle itself.
When a gang do es not span all CPUs, corresp onding con trollers can b e activ ated,
so that this otherwise w asted computing p o w er is sp en t on smaller gangs. This
is called sele ctive disabling and causes a fairness sk ew to w ards smaller gangs.
Notew orth y is, that it is explicitly left op en whether the con troller net w ork
is a logical or an actual ph ysical net w ork. Realizing the con troller net w ork
separately from the CPUs of the system pro vides t w o adv an tages: there is less
in terference with user co de, and – due to the tree b eing balanced – sc heduling
decision arriv e at all affected CPUs at the same time, indep enden t of the net w ork
latency . The disadv an tage is its static structure once created.
DHC considers basically t w o t yp es of fairness: giving eac h application the
same amoun t of system time (called uniform), and giving eac h task the same
amoun t of system time (called w eigh t). Giving eac h application the same amoun t
of CPU time is discarded as b eing to o wasteful despite of their selecting disabling,
whic h coun ters that. Load im balances b et w een child con trollers in the uniform
case are solv ed b y fa v oring gangs in the lesser loaded c hild compared to all other
gangs, i. e., another fairness skew to w ards smaller gangs. Finally , the w eigh t
case fa v ors gangs that fill or almost fill their controller o v er other gangs.
DHC only balances newly created gangs. Migration is not considered. In the
first description [43] of DHC, this balancing or mapping is done decen trally b y
searc hing the vicinit y of the curren t con troller for the least loaded con troller. T o
supp ort that more efficien tly , the binary tree is augmen ted with additional links,
connecting con trollers within the same lev el. This leads, e. g., to an X-tree [71].
T o impro v e selectiv e disabling, individual tasks within a gang are mapp ed to
CPUs so that as man y con trollers as p ossible are kept free. The definition of
load includes only the load b elo w a con troller and ignores im balances within the
con troller hierarc h y completely . Th us, this metho d migh t mak e decisions that
increase the sk ew of load.
In [70] an alternativ e is suggested that starts at the ro ot con troller and alw a ys
selects the least loaded c hild con troller. This solv es one of the aforemen tioned
problems, but could still increase fragmen tation instead of reducing it, when a
gang is mapp ed to a con troller whose sibling has no load. A cen tral strategy for
mapping applications in DHC is suggested in [72]. Esp ecially , it pa ys atten tion
to reduce im balances first, if p ossible.
56 CHAPTER 4. COSCHEDULING APPR O A CHES
In terms of Chapter 3, DHC supp orts only flat cosc heduled sets with a strict
cosc heduling constrain t. Other time constrain ts are not supp orted. Though not
in the scop e originally , limited supp ort for placemen t constrain ts can b e realized
b y matc hing the system top ology with the con troller hierarc h y – p ossibly gener-
alizing the idea to non-binary trees. Then, dep ending on the mapping, either the
close or indep enden t placemen t constrain t can b e realized. A dditionally , with-
out selectiv e disabling and an exact matc h of system top ology and con troller
net w ork, the isolation constrain t can b e realized.
4.3 Distributed Queue T ree
The Distributed Queue T ree (DQT) approac h b y Hori et al. [44, 45] shares man y
similarities with DHC. Both are examples for a sc heduling class, whic h Hori et
al. dubb ed Time Space Sharing Sc heduling (TSSS).
A t its core, TSSS refers to all those sc hedulers, which com bine time sharing
and space sharing, so that partitions of a parallel system are rep eatedly allo cated
to parallel applications. Glossing o v er a few details, suc h as global time slots
or some requiremen ts for the used comm unication net w ork, some instances of
the sc heduling approac h in this thesis could b e considered to fall into the TSSS
category . While TSSS explicitly honors top ology , it only supp orts flat cosc hed-
uled sets: m ultiple partitions are indep enden t from eac h other. As partitions are
assigned exclusiv ely , this realizes the resource sharing and isolation constrain ts.
TSSS targets F eitelson’s gang sc heduling, that is, a realization of TSSS has to
supp ort at least the cosc heduling constrain t.
DQT as an instan tiation of TSSS is effectiv ely DHC with a con troller net-
w ork organized along the system top ology , without selectiv e disabling, and with
its o wn set of rules for sc heduling and (static) load balancing. The size of a
cosc heduled set m ust b e known a priori and is then fixed. DQT as a concept
mak es no further sp ecification on ho w a parallel application is sc heduled within
its partition. With the approac h targeting uniform m ultiple instruction/m ultiple
data (MIMD) systems, migration is considered out of scop e.
4.4 Cosc heduling in VMw are ESXi
The h yp ervisor in VMw are’s vSphere, named ESX or ESXi dep ending on the
v ersion, is the only realization of a cosc heduler in a commercially successful pro d-
uct. The sc heduler itself [9, 68] has c hanged drastically across v ersions. With its
v arious configuration p ossibilities a description capturing all of its functionalit y
is out of the scop e, here. Instead, w e will fo cus on just a few sp ecific asp ects of
the cosc heduler.
Since ESX 2.x a relaxed cosc heduling constrain t is supp orted. It started out
quite strict – but not fully meeting the requiremen t for strict constrain ts giv en
in Chapter 3 – and got more and more relaxed since then. Initially in ESX 2.x,
4.5. FUR THER CONSIDERA TIONS 57
runnable vCPUs of a VM had to start execution sync hronously but w ere allow ed
to get preempted indep enden tly – unless the preempted vCPU w as lagging to o
far b ehind, in whic h case all vCPUs w ere preempted. With ESX 3.x this w as
relaxed and – while cosc heduling is still maximized when p ossible and sets with
more tasks than CPUs are still forbidden – the constrain t is no w more akin to a
minimal concurrency constrain t: at least the vCPUs, whic h are lagging b ehind,
ha v e to b e cosc heduled. Sc heduling more vCPUs is just considered a b on us. In
ESX 4.x this w as con v erted again: instead of forcing the execution of da wdling
vCPUs, fast-paced vCPUs are no w prev ented from getting executed. That is,
instead of a minimal concurrency constrain t, a selectiv e maximal concurrency
constrain t is applied un til the other vCPUs ha v e caugh t up. Note, that together
with these c hanges, there w ere also c hanges with resp ect to what is considered
progress for a vCPU, but these details do not matter for the high-lev el o v erview
here.
ESXi has also supp ort for placemen t constrain ts, though the supp orted con-
strain ts also v ary across v ersions. Up to ESX 3.x, there w as the concept of
sche duler c el ls , whic h are iden tically sized groups of (ph ysical) CPUs, whic h
(usually) honor the system top ology . Th us, they represen t a (real or artificially
inserted) lev el in the top ology tree. ESX 3.x do es not allo w mapping of cosc hed-
uled sets to top ological units ab ov e the sc heduler cells, hence, cosc heduled sets
implicitly ha v e strict resource sharing constrain ts. ESX 4.0 ab olished sc heduler
cells and adopted a relaxed indep enden t placemen t p olicy of cosc heduled sets
within top ological units b elo w NUMA domains and (since ESX 4.1) a strict close
placemen t p olicy for cosc heduled sets ab o v e NUMA domains. Optionally , the
former can b e replaced b y a relaxed close placemen t constrain t. With ESXi 5.x
the load balancing algorithm w as o v erhauled. While the high-lev el placemen t
goals of cosc heduled sets remain the same, the placemen t has b ecome sligh tly
more con ten tion-a w are. Also, it is no w p ossible to exp ose the NUMA top ology
of a cosc heduled set to the VM it represen ts – an example for nested cosc heduled
sets.
4.5 F urther Considerations
All presen ted approac hes require an explicit notion of coscheduled sets. In their
originally in tended application area these are easily iden tified: one cosc heduled
set p er parallel application. Ho w ev er, with all use cases and to da y’s general
purp ose systems in mind, this is no longer go o d enough. Basically , researc h
branc hes out in three differen t directions from here.
Firstly , it is p ossible to generate cosc heduled sets automatically . This can
range from the more simplistic auto-grouping done b y Lin ux for accoun ting
purp oses o v er automatic grouping [73] or top ology-a w are nesting [74] via instru-
men ted parallel programming en vironmen ts to the wide area of resource-a w are
sc heduling (cf. Section 2.2). With resp ect to this thesis, the automatic gener-
58 CHAPTER 4. COSCHEDULING APPR O A CHES
ation of cosc heduled sets is considered out-of-scop e – though the in tegration of
suc h approac hes is discussed later in Chapter 11.
Secondly , with giv en cosc heduled sets it is still p ossible to driv e their time
and placemen t constrain ts dynamically at run time. This is of particular in terest,
when cosc heduled sets w ere the result of some more primitiv e heuristic or when a
set c hanges its b eha vior and it is not desired to reform the sets in the system. F or
example, in flexible c osche duling [75, 76] the cosc heduling constrain t is tied to a
feedbac k lo op monitoring the set. Similarly , this can also b e done for placemen t
constrain ts, as the AD APT framew ork [77] demonstrates.
Thirdly , there is researc h whic h addresses shortcomings of the presen ted ap-
proac hes. F or instance, there is p air e d gang sche duling [78], whic h addresses
missing supp ort for in teractiv e w orkloads b y pairwise merging of I/O in tensiv e
and CPU in tensiv e cosc heduled sets – and losing the abilit y for strict cosc hedul-
ing along the w a y . With c ontr ol le d c ontention [79] the absence of dynamic place-
men t constrain ts is comp ensated b y opp ortunistically merging cosc heduled sets
un til enough load has b een accum ulated to k eep a partition busy . Another ex-
ample is b alanc e d sche duling [80], a probabilistic (i. e., v ery relaxed) cosc heduling
approac h, whic h a v oids sync hronization b y ditc hing time constrain ts completely
and relying only on placemen t constrain ts, so that no t w o tasks within a set
end up on the same CPU. Strictly sp eaking, this thesis itself also falls in to the
last category: unify previously indep enden t researc h and pro vide an approac h,
whic h is ready for to da y’s w orkloads on con temp orary systems.
4.6 Chapter Notes
This concludes the in tro ductory part of this thesis. The second part presen ts m y
cosc heduling approac h and its realization, which addresses shortcomings of the
approac hes presen ted here and also unifies other sc heduling ideas. Later, as part
of individual c hapters of the third part, more sp ecialized sc heduling approac hes
will b e discussed and their relation to m y approac h.
where a new cosc heduler is motiv ated, designed,
implemen ted, and ev aluated
P art I I
Cosc heduler Design
59
T able of Con ten ts
5 Design Rationales 63
5 . 1 V e r s a t i l i t y ............................... 6 3
5 . 2 S c a l a b i l i t y ............................... 6 4
5 . 3 I n t e r a c t i v i t y.............................. 6 4
5 . 4 N o n - i n t r u s i v e n e s s ........................... 6 5
5 . 5 C h a p t e r N o t e s ............................. 6 5
6 T op ology-a w are Cosc heduling 67
6 . 1 B a s i c S t r u c t u r e ............................ 6 7
6.1.1 Sync hronization Domains . . . . . . . . . . . . . . . . . . 67
6 . 1 . 2 S c h e d u l i n g ........................... 6 9
6 . 1 . 3 F a i r n e s s ............................ 7 0
6.2 Mapping of Coscheduled Sets . . . . . . . . . . . . . . . . . . . . 74
6.2.1 Time constraints . . . . . . . . . . . . . . . . . . . . . . . 75
6.2.2 Placemen t constrain ts . . . . . . . . . . . . . . . . . . . . 76
6 . 3 L o a d B a l a n c i n g ............................ 7 7
6.4 F ragmen tation a v oidance . . . . . . . . . . . . . . . . . . . . . . . 79
6 . 4 . 1 I d l e s e l e c t i o n ......................... 8 0
6.4.2 Asymmetric SD shap es . . . . . . . . . . . . . . . . . . . . 80
6.5 T rac king of CPU time . . . . . . . . . . . . . . . . . . . . . . . . 81
6 . 6 C h a p t e r N o t e s ............................. 8 4
62 T ABLE OF CONTENTS
7 In tegrating T A CO 85
7.1 Requiremen ts for In tegration . . . . . . . . . . . . . . . . . . . . . 85
7.2 Con v ersion of Non-cosc heduling Sc hedulers . . . . . . . . . . . . . 86
7.3 Cosc heduling in Lin ux . . . . . . . . . . . . . . . . . . . . . . . . 89
7.3.1 Lin ux Sc heduler Basics . . . . . . . . . . . . . . . . . . . . 89
7.3.2 Sc heduled T ask Groups . . . . . . . . . . . . . . . . . . . . 93
7 . 3 . 3 L o a d B a l a n c i n g ........................ 9 5
7 . 3 . 4 C u r r e n t S t a t e ......................... 9 7
7.4 Cosc heduling in F reeBSD . . . . . . . . . . . . . . . . . . . . . . . 98
7.4.1 F reeBSD Sc heduler Basics . . . . . . . . . . . . . . . . . . 98
7 . 4 . 2 D e s i g n O u t l i n e ........................ 1 0 0
7.4.3 Comparison with Linux In tegration . . . . . . . . . . . . . 103
7 . 5 C h a p t e r N o t e s ............................. 1 0 4
8 Analysis and Ev aluation 105
8.1 Collectiv e Con text Switch . . . . . . . . . . . . . . . . . . . . . . 106
8 . 1 . 1 D i r e c t C o s t s .......................... 1 0 6
8.1.2 Indirect Costs . . . . . . . . . . . . . . . . . . . . . . . . . 108
8 . 2 F r a g m e n t a t i o n ............................. 1 0 9
8.2.1 In ternal F ragmen tation . . . . . . . . . . . . . . . . . . . . 109
8.2.2 External F ragmen tation . . . . . . . . . . . . . . . . . . . 110
8 . 3 P r e e m p t i o n .............................. 1 1 1
8 . 4 E x p e r i m e n t s .............................. 1 1 2
8.4.1 Cosc heduling of Virtual Mac hines . . . . . . . . . . . . . . 112
8.4.2 Cosc heduling of P arallel Programs . . . . . . . . . . . . . . 113
8.4.3 Un used Cosc heduling F unctionalit y . . . . . . . . . . . . . 116
8 . 5 C h a p t e r N o t e s ............................. 1 1 7
Chapter 5
Design Rationales
Lo oking at existing approac hes of creating a sc heduler capable of cosc heduling,
they can b e group ed into a few differen t categories. Man y early or ad-ho c
cosc heduling approac hes are quite inflexible: they can only cosc hedule across
the whole system, are not able to sc hedule tasks sim ultaneously that b elong to
differen t applications, or lac k supp ort to handle legacy situations. Approac hes
dev elop ed for sp ecific use cases, suc h as the virtual mac hines or the a v oidance
of resource con ten tion, ha v e a quite limited scop e, whic h often mak es them
unsuitable for other use cases. Some approac hes require supp ort b y applications
themselv es, which mak es them unsuitable for unmo dified applications. While
dra wbac ks can b e addressed individually , these mo dified solutions often also
in tro duce differen t restrictions, whic h can b e limiting in other scenarios.
A new cosc heduler shall b e dev oid of these dra wbac ks, motiv ating the fol-
lo wing design rationales.
5.1 V ersatilit y
The cosc heduler shall supp ort a wide v ariet y of use cases. There are just to o
man y p ossibilities to apply cosc heduling. Supp orting only a subset of use cases
will lead to a situation, where only parts of a system or only a few applications
are able to gain b enefits. Other cosc heduling-based optimizations are prev en ted
and applications are barred from realizing more efficien t solutions. Ov erall, a
system will not b e able to reac h an optimal op erating p oin t with a limited
cosc heduler. This also means that the coscheduler has to support different use
cases sim ultaneously . The realization of one use case should not prev en t the
realization of differen t use cases in a differen t part of the system, at a differen t
time, or a differen t abstraction lev el.
V ersatilit y is ac hieved, when arbitrary , p ossibly nested cosc heduled sets can
b e sp ecified and ev ery sc heduling constrain t is supp orted at least in its strict
or ev en precise v arian t. Ideally , adherence lev els can b e sp ecified at constrain t
lev el, giving the sc heduler more optimization opp ortunities.
63
64 CHAPTER 5. DESIGN RA TIONALES
5.2 Scalabilit y
With “m ulticore” slo wly transitioning to “man ycore”, the cosc heduler shall b e
scalable. Scalabilit y is a delicate topic for cosc heduling: exp erience with clus-
ters has sho wn, that the collectiv e con text switch can b ecome problematic when
scaling up – latency gets higher and sim ultaneousness suffers. F or clusters,
this w as addressed in v arious w a y: the dev elopmen t of implicit sc heduling tec h-
niques, relying on sync hronized clo c ks, or the in tro duction of hardw are supp ort.
Eac h solution has its o wn dra wbac ks: the first limits versatilit y , the second can-
not handle sp on taneous ev en ts, and the third is not a v ailable in ev ery cluster.
Luc kily , comm unication within m ulticore systems is m uc h less exp ensiv e than in
clusters. That is, while tec hnically not true, m ulticore systems can b e considered
to ha v e in tegrated supp ort for sync hronized con text switc hes in the form of in ter
pro cessor in terrupts (IPIs).
F or cosc heduling on larger systems, there are t w o types of scalability to
consider. On the one hand, p erformance of iden tically sized partitions should
not suffer, just b ecause a system is larger. On the other hand, it should b e
p ossible to create larger cosc heduled sets on larger systems without running in to
some upp er limit.
Scalabilit y is ac hiev ed, when coscheduled sets are unrestricted and op eration
costs are indep enden t from the size of the system. Op eration costs ma y , ho w ev er,
dep end on the size of cosc heduled sets. Though, ev en the largest cosc heduled
set should still b e handled without prohibitiv e o v erhead.
5.3 In teractivit y
In teractiv e b eha vior is considered b y man y cosc heduling approac hes as a sec-
ond class citizen: cross application sync hronization of time-slices, rigid sc hed-
ules (e. g., Ousterhout matrix), and from a to da y’s viewp oin t ridiculous large
time-slices (sometimes measured in min utes) do not w ork w ell together with in-
teractivit y requiremen ts. This is unfortunate as man y m ulticore systems to da y
are used in teractiv ely: from desktops to serv ers to mobile devices.
The cosc heduler shall imp ose no restrictions on in teractiv e usage. In teractiv e
(or I/O in tensiv e) tasks should still b e able to b e executed on a rather short
notice, as it is accomplished b y most sc heduling strategies, so that short resp onse
times are realized. Note, that in teractiv e b eha vior is not limited to sequen tial
tasks: whole cosc heduled sets migh t w ak e up for short parallel execution phases.
In teractivit y is ac hiev ed, when in teractiv e scenarios can b e realized without
crippling v ersatilit y .
5.4. NON-INTR USIVENESS 65
5.4 Non-in trusiv eness
Finally , the last rationale shifts the p oint of view from users or administrators
to the dev elop ers of sc hedulers.
The cosc heduler shall in tegrate itself nicely in to existing sc hedulers (or allo w
a maxim um degree of freedom for y et-to-b e-written sc hedulers). That is, in
order to add the feature of cosc heduling to an existing sc heduler, it should not b e
necessary to en tirely replace or rewrite the existing sc heduler. Ideally , all features
of said sc heduler sta y in tact and the only c hange is its sudden cosc heduling
capabilit y . F or new sc hedulers, there shall b e no imp osed restrictions regarding,
e. g., the c hoice of runqueue or balancing algorithms.
Non-in trusiv eness is ac hiev ed, when all normally existing sc heduling features
can still b e realized (and p ossibly existing co de reused). This esp ecially means,
that legacy use cases, whic h migh t ha v e b een tailored to w ards the original sc hed-
uler, are still fully supp orted – just as b efore. A non-in trusiv e cosc heduler is
app ealing for users and dev elop ers alik e, b ecause it keeps pro v en co de in tact
and allo ws to in tro duce the new b eha vior gradually .
5.5 Chapter Notes
The rationales presen ted in this c hapter form the guidelines for m y cosc heduler
that is designed, implemen ted, and analyzed in the follo wing three chapters.
The rationales are all motiv ated b y m y o wn exp eriences when I got exp osed
to the topic as part of m y researc h on the influence of sc heduling on energy
consumption on con temp orary pro cessors. At some p oin t cosc heduling came up
as a mec hanism, that – theoretically – should b e b eneficial in the considered
scenario, if used just right. But ho w do y ou actually pro v e it practically? No
curren tly a v ailable op erating system supp orts cosc heduling, so the simple route
do es not w ork. Then y ou lo ok at other p eople’s w ork: for this one, y ou w ould
ha v e to express y our w orkload in form of virtual mac hines in tro ducing another
bunc h of problems; that one lo oks promising but cannot handle quic kly c hang-
ing requiremen ts; the other one is able to do that, but cannot handle parallel
applications, and so on. Man y go o d ideas that – while solving the issue they
w ere designed for – could not b e applied to m y problem. So, what’s next? A dd
m y o wn sp ecialized solution to the p o ol and mo v e on? Or sp en t some more time
and actually do something ab out it?
I decided to do something ab out it and shifted m y researc h fo cus on co-
sc heduling itself. The result so far is captured in this thesis. The original
problem is no w a small part of it and presen ted later in Chapter 10.
66 CHAPTER 5. DESIGN RA TIONALES
Chapter 6
T op ology-a w are Cosc heduling
Based on the previously describ ed design rationales, this c hapter presen ts
a new design for a cosc heduler. While the o v erall goal is the same as of an y
cosc heduler, namely executing certain tasks sim ultaneously , the design presen ted
here differs from previously presen ted designs substan tially . The ma jor reason for
this difference is that it do es not try to solv e a single problem with cosc heduling,
but that it is able to handle most – if not all – use cases one can think of on
curren t m ulticore arc hitectures.
This c hapter starts with a description of the general structure of the de-
sign and the emplo y ed algorithms in Section 6.1. After this, the construction of
cosc heduled sets within that structure is discussed in Section 6.2. Section 6.3
co v ers the load balancing of these cosc heduled sets. After that, differen t p ossibil-
ities to handle fragmen tation are sub ject of Section 6.4. The c hapter closes with
an alternativ e accoun ting realization, whic h eliminates the remaining fairness
and balancing issues in Section 6.5.
6.1 Basic Structure
T o facilitate a scalable cosc heduler, the core of the design consists of the con-
cept of synchonization domains (SDs) and rules on ho w they are created, link ed,
and selected. In a sense, sync hronization domains are partitions on steroids.
Sync hronization only happ ens within a SD. Th us, indep enden t SDs op erate au-
tonomously in a large system.
6.1.1 Sync hronization Domains
A SD is describ ed b y a set of CPUs. In that sense, it is similar to a partition.
Ho w ev er, it is also more than that. A SD is sc heduled and in turn also sc hed-
ules other SDs in its place. So, the structure of SDs go es b ey ond partitioning
sc hemes as describ ed in, e. g., [65]. Sync hronization activities b y the sc heduler
67
68 CHAPTER 6. TOPOLOGY-A W ARE COSCHEDULING
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
(a) One SD at system lev el (e. g., Ousterhout matrix). Arbitrarily large
cosc heduled sets, but sync hronization do es not scale.
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
(b) One SD p er pro cessor (e. g., VMw are ESX 3.x). Sacrifice maximal set size
for b ounded sync hronization costs.
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
CPU
(c) One SD p er CPU (e. g., Linux Completely F air Sc heduler). No cosc hedul-
ing, no sync hronization.
Figure 6.1: Scaling a fictional system with dual-core pro cessors using differen tly
sized SDs. There is a trade-off b et w een sync hronization costs and the maximal
size of a cosc heduled set. T o get the b est of b oth w orlds, SDs of differen t sizes
m ust b e supp orted at the same time with transparen t switc hing b et w een them
(not sho wn).
are confined to the b oundaries of a SD. On the one hand, this means that in-
dep enden t SDs mak e progress on their o wn. On the other hand, it means that
cosc heduling can only happ en within a SD b ecause of the required sim ultaneous
con text switc h.
Setups supp orting only a single, system-wide sync hronization domain (e. g.,
ev erything based on an Ousterhout matrix) are inheren tly unscalable, as syn-
c hronization o v erhead within the SD will increase with larger systems. The other
extreme is supp ort for small SDs only , suc h as one SD p er pro cessor. While this
do es not cause increased sync hronization o v erheads on larger systems, it prev en ts
the creation of larger cosc heduled sets. Op erating systems without supp ort for
cosc heduling can b e seen as supp orting SDs with just one CPU p er SD. These
three cases are illustrated for a fictional system with dual-core pro cessors in
Fig. 6.1. Thus, a v ersatile system m ust b e able to supp ort large and small SDs,
and it m ust b e able to switc h b et w een them during run time. And if interactivit y
is a criterion, this switc hing m ust happ en quite fast and on-demand.
Eac h SD comes with its o wn runqueue and do es its o wn sc heduling. Theoret-
ically , different sync hronization domains could ev en utilize differen t sc heduling
algorithms. A sc heduling decision within a SD affects exactly those CPUs, whic h
are part of the SD; not more, not less. Elemen ts within a runqueue are usually
references to other, probably smaller SDs – except for elemen t within SDs span-
ning only a single CPU, whic h ma y also reference tasks. Cosc heduled sets are
6.1. BASIC STR UCTURE 69
mapp ed to sync hronization domains. When there are no suitable SDs, new SDs
m ust b e created and in tegrated in to the already existing SD structure.
Initially , the system con tains one system-wide SD, whic h is empt y , i. e., the
system is idle. F urther SDs are added as c hildren of existing SDs. The CPUs of
a c hild SD m ust b e a subset of the CPUs of the paren t SD. If a c hild SD do es
not co v er all CPUs of the paren t, it m ust b e part of group of m ultiple c hild SDs
that are pairwise disjoin t and co v er all CPUs of the paren t. This leads to a tree-
lik e structure of progressiv ely smaller sync hronization domains. Within this SD
structure, it is p ossible to differen tiate t w o t yp es of paren t/c hild relationships:
V ertical relationships: A v ertical relationship splits a larger SD in to m ultiple
smaller SDs. SDs in a v ertical relationship alw a ys b elong to the same
cosc heduled set. The tree-lik e represen tation of a system’s top ology giv es
a go o d idea of v ertical relationships.
Horizon tal relationships: A horizon tal relationship connects SDs b elonging
to differen t cosc heduled sets. Without loss of generalit y , w e assume in this
thesis, that SDs in a horizon tal relationship are of the same size, whic h
simplifies the up coming descriptions and diagrams.
With this hierarc h y of progressiv ely smaller SDs, sync hronization within the
system is reduced to the necessary minim um. The deep er SDs are within the
hierarc h y , the less CPUs ha v e to b e sync hronized during context switc hes within
those SDs.
6.1.2 Sc heduling
Eac h SD has its o wn runqueue. This runqueue is used to co ordinate c hild SDs
and – when the SD is resp onsible for only one CPU – tasks. Eac h elemen t in
the runqueue references w ork for all CPUs of the SD. That is, c hild SDs of a
smaller size are only en tered in form of a group. T o determine whic h task
should b e executed on a certain CPU, the SD hierarc h y is pro cessed from top
to b ottom. In eac h SD, one elemen t from within the runqueue is pic k ed and the
selection pro cess con tin ues recursiv ely with the SDs represented b y this elemen t.
This con tin ues un til leaf SDs are reac hed, where finally tasks are selected. Ev ery
pic k ed SD along the paths bac k to the top is considered running.
Con ten tion is a v oided b y designating a CPU as master for each SD, whic h
is resp onsible for co ordinating the corresp onding CPUs. Being master is not a
fixed role; instead, this role can b e freely transferred b et w een CPUs b elonging
to the SD. The master examines the runqueue, picks c hildren to execute, and
notifies masters of selected c hildren, so that they will do their part of the task
selection. The master is also resp onsible to enforce preemption after the time-
slice has b een used up. Ev ery CPU is the master of some SDs – a least of those
co v ering just the CPU in question.
Eac h CPU holds a reference to the top-most running SD for whic h it curren tly
has master resp onsibilities; this SD is the CPU’s p ersonal r o ot within the SD
70 CHAPTER 6. TOPOLOGY-A W ARE COSCHEDULING
hierarc h y . When a CPU receiv es the prop er notification (e. g., via IPI for a
preemption, or via timer in terrupt for a used-up time-slice), it starts a task
selection at its p ersonal ro ot: the CPU pic ks an elemen t from the runqueue,
up dates the p ersonal ro ots of master CPUs (other than itself ) of c hild SDs
referenced b y the pic k ed elemen t, and notifies those masters afterw ards, so that
they start a task selection of their o wn, in addition to con tin uing do wn w ards
itself. This w a y , the selection pro cess is decen tralized and triggered on-demand.
When there is only one elemen t in the upp er lev els of the SD hierarc h y and
when there are no c hanges, there is no need to examine them rep eatedly . Hence,
they do not cause o v erhead, when they are not activ ely required. If at an y p oin t
in the hierarc h y an empt y runqueue is found, it means that the CPUs of the
SD in question are idle. If load balancing is also unable to find some w ork and
no other tec hniques are applied (see Section 6.4), the CPUs of the SD m ust b e
notified so that they preempt the curren tly running task in fa v or of an idle lo op
and p ossibly en tering a p o w er sa v e mo de.
While selecting a task pro ceeds from top to b ottom, enqueuing and dequeuing
of tasks pro ceeds from b ottom to top of the SD hierarc h y . When a task b ecomes
runnable, it is inserted in to a leaf SD. This in turn migh t mak e the SD itself
runnable. When that is the case, the group con taining this SD is inserted in to the
paren t. This pro cess is rep eated, until the top is reac hed. As higher lev els in the
SD hierarc h y represen t m ultiple CPUs, it is lik ely that this pro cess encoun ters a
SD that is already inserted in to its paren t runqueue. In this case, the pro cess is
stopp ed early . Th us, enqueuing do es usually not tra v erse the whole hierarc hy .
Dequeuing w orks similarly . The task is first remov ed from its leaf SD. If the leaf
SD itself can no longer b e considered runnable, it is remov ed from its paren t –
and so on.
The pro cess of enqueuing and dequeuing a task migh t trigger a preemption.
Either b ecause a SD b ecomes runnable that is more imp ortan t than the cur-
ren tly running one, b ecause the curren tly running tasks b elong to a no longer
runnable SD, or b ecause of a c hange in imp ortance due to the enqueuing or
dequeuing. P erforming these preemption c hec ks migh t require to tra v erse the
hierarc h y further, dep ending on the actually emplo y ed preemption rules. If a
CPU considers a preemption to b e advisable, this is a go o d p oin t for the CPU to
tak e o v er the master role and trigger the preemption itself rather than notifying
the curren t master first.
6.1.3 F airness
A c hieving fairness and load balancing in this kind of setup is less trivial than
in other sc heduling sc hemes. There are sev eral issues to address, foremost con-
cerning fairness. When the fairness question is answ ered, the load balancing is
more or less a result of this.
Because of the p ossibilit y of a sc heduling en tit y represen ting v arying amoun ts
of sequen tial or parallel applications p ossibly spanning m ultiple CPUs, it m ust
6.1. BASIC STR UCTURE 71
b e p ossible to assign differen t amoun ts of CPU time to differen t SEs. This can
b e done b y assigning a w eigh t to ev ery SE and implemen t a prop ortional-share
sc heduling. By con trolling this w eigh t, a single en tit y can represen t v arious
amoun ts of load and it is also p ossible to realize differen t in terpretations of
fairness. Th us, the prop osed sc heme is similar in spirit to W aldspurger’s stride
sc heduling [81, 82] or the Lin ux Completely F air Sc heduler [83]. Though the
similarities end at the hierarc hical m ulticore setup. This thesis uses the follo wing
naming con v en tions:
Definition 14 (W eigh t of a sc heduling en tit y) . Eac h sc heduling en tit y se has
an asso ciated w eigh t w . (The w eigh t itself dep ends on the t yp e of SE and will
b e concretized later.)
w eig ht ( se ) = w
Definition 15 (W eigh t of a runqueue) . The w eigh t of a runqueue r q is the sum
of w eigh ts of all enqueued sc heduling en tities se i .
w eig ht ( r q ) = ∑︂
se i ∈ r q
w eig ht ( se i )
Definition 16 (Share of a sc heduling en tity) . The share of a sc heduling en tit y
se i is its fraction of the total w eigh t of the runqueue r q , where it is enqueued in.
shar e ( se i ) = w eig ht ( se i )
w eig ht ( r q )
The sum of all shares of a particular runqueue is one. The time, that a run-
queue receiv es (b ecause its SD w as pic k ed), is distributed among the enqueued
en tities according to this share. The metho d, how the time distribution is actu-
ally realized, do es not really matter. F or instance, W aldspurger mo difies the task
selection frequency , while Lin ux additionally uses differen tly sized time-slices.
Ev ery runqueue (except the top runqueue of the ro ot set) is represen ted
b y another sc heduling en tit y in its paren t SD. F or horizon tal connections, this
is a one-to-one mapping, for v ertical connections a man y-to-one mapping, i. e.,
a sc heduling en tit y represen ts m ultiple runqueues. The w eigh t of suc h a SE
dep ends on what it represen ts and what is considered fair. A SE ma y represen t:
a single task, a parallel application (consisting of m ultiple SEs), or a bunc h of
unrelated sequen tial or parallel applications. In case it represen ts m ultiple SEs,
these migh t or migh t not b e distributed o v er m ultiple CPUs. As an extension, a
SE migh t also represen t only a fraction of a parallel application. These SE t yp es
are confron ted with differen t t yp es of fairness. Typically it is either fairness at
task lev el, fairness at application lev el with resp ect to CPU time, or fairness at
application lev el with resp ect to system time. (Other kinds of fairness, suc h as
fairness at user lev el, can b e mo deled with this, to o: the user is then simply an
artificial application consisting of m ultiple other SEs.)
The results of applying all three t yp es for fairness in differen t situations are
depicted in Fig. 6.2. F airness at task lev el (or SE lev el) is appropriate, when the
72 CHAPTER 6. TOPOLOGY-A W ARE COSCHEDULING
C P U 1
C P U 2
A B 1 C 1 C 3
B 2 C 2 C 4
(a) F airness at task lev el: ev ery task gets the same amoun t of CPU time.
C P U 1
C P U 2
A B 1 C 1 C 3
B 2 C 2 C 4
(b) F airness at group lev el with resp ect to CPU time: ev ery group gets the same
amoun t of CPU time.
C P U 1
C P U 2
A B 1 C 1 C 3
B 2 C 2 C 4
(c) F airness at group lev el with resp ect to system time: ev ery group gets the same
amoun t of system time.
Figure 6.2: Differen t t yp es of fairness applied to certain groups of SEs. Group A :
a single task. Group B : m ultiple tasks, but not more tasks than CPUs. Group C :
m ultiple tasks, more tasks than CPUs.
SEs in a group are indep enden t from eac h other. Within the SD structure, this
is the case for v ertical connections, where a SE represen ts m ultiple indep enden t
runqueues, eac h with p ossibly m ultiple indep enden t SEs. F airness at group
lev el is appropriate, when there is a close relationship b et w een the SEs within
the group, e. g., parallel applications. A fair distribution of CPU time among
groups is useful for malleable applications: the computational resources eac h
application receiv es are the same, no matter ho w man y tasks. With t ypically
sub-linear sp eedups, this promotes the idea of using as few CPUs as p ossible,
unless there are some circumstances whic h allo w a higher p erformance, suc h as
a higher amoun t of com bined cache. This is exploited later in Chapter 9. A fair
distribution of system time among groups can b e considered traditional, as this is
what all partitioning approac hes do as w ell as Ousterhout’s matrix metho d and
F eitelson’s Distributed Hierarc hical Con trol (cf. Chapter 4). It provides size-
indep enden t fairness for rigid applications without ha ving to resort to w eight
manipulations. In the con text of this thesis, it has no use.
T ask lev el fairness is ac hiev ed b y an aggr e gating SE . An aggregating SE is
transparen t from a CPU time p ersp ectiv e: a share of an y of the represen ted SEs
is directly comparable to a share of a SE enqueued in the same runqueue as
the aggregating SE. Equal shares at b oth lev els indicate that b oth SEs receiv e
an equal amoun t of CPU time (giv en p erfectly balanced load). T o ac hiev e this,
the w eigh t of the aggregating SE m ust b e equal to the sum of all represen ted
w eigh ts.
6.1. BASIC STR UCTURE 73
Definition 17 (W eigh t of an aggregating SE) . The w eigh t of an aggregating
SE se is the sum of w eigh ts of all runqueues r q i represen ted b y this SE.
w eig ht ( se ) = ∑︂
r q i ∈ se
w eig ht ( r q i )
Note, that this is only exact, when there is no load im balance. When an
aggregating SE is pic k ed during sc heduling, ev ery runqueue represen ted b y it
gets the same amoun t of CPU time. Hence, if the load is not distributed ev enly
(e. g., one CPU with three tasks and one with t w o tasks), some SEs will receiv e
more CPU time than indicated b y their share and others less. Still, in this case
it is sufficien t to address the im balance only within the group of represented
runqueues b y , e. g., p erio dic rebalancing. This is differen t, when there is not
enough load to o ccup y all CPUs. Sp ecifically , the situation sho wn in Fig. 6.2a
w ould ha v e b een realized differen tly: assuming a w eigh t of one for eac h task,
the w eigh t of an aggregating SE for group A is 1, for B 2, and for C 4. Th us,
A w ould get only half of the depicted time with the ab o v e form ula, and no
rebalancing within the group w ould b e able to address that. That is, fairness is
sk ew ed to w ards larger applications. One p ossibilit y to resolv e this, is to accoun t
for empt y runqueues as done in the follo wing revised definition.
Definition 18 (W eigh t of an aggregating SE, revised) . The w eigh t of an aggre-
gating SE se is the sum of w eigh ts of all non-empt y runqueues r q i represen ted
b y this SE scaled to the o v erall n um b er of runqueues.
w eig ht ( se ) = |{ r q ∈ se }|
|{ r q ∈ se : r q non-empt y }| · ∑︂
r q i ∈ se
w eig ht ( r q i )
Another p ossibilit y to handle this problem is to incorp orate the abilit y to
handle an y differences of exp ected and receiv ed CPU time in to the sc heduling
algorithm itself, whic h is discussed later in Section 6.5. It should b e noted, that
an y w a y to ensure fairness in suc h a case also increases fragmen tation. Therefore,
the sc heduler should mak e sure to not get into suc h a situation in the first place
(cf. related discussion in Chapter 9).
F airness at application lev el is ac hiev ed b y gr oup SEs , whic h are used for
horizon tal connections. In the simplest case, an application is represen ted b y
just one SE. Then, the w eigh t just dep ends on the desired t yp e of fairness.
Definition 19 (W eigh t of a group SE (fair CPU time)) . The w eigh t of a group
SE se for a fair distribution of CPU time is a constan t w eigh t w c , completely
ignoring the SEs it represen ts and where in the SD hierarc hy it is enqueued.
w eig ht ( se ) = w c
Group SEs with iden tical w eigh ts receiv e the same amoun t of CPU time,
giv en a p erfectly balanced load. If a fair distribution of system time is desired
instead, the w eigh t has to b e scaled with the n um b er of CPUs, i. e., the “system”
the group SE is asso ciated with.
74 CHAPTER 6. TOPOLOGY-A W ARE COSCHEDULING
Definition 20 (W eigh t of a group SE (fair system time)) . The w eigh t of a group
SE se for a fair distribution of system time is a constan t w eigh t w c , scaled to
the n um b er of CPUs the SE represen ts – its capacit y .
w eig ht ( se ) = w c · capacity ( se )
Un til no w, a group SE represen ts something lik e a parallel application in its
en tiret y . This singular represen tation is what realizes cosc heduling in the end.
Ho w ev er, when cosc heduling is not necessary , or only smaller sub-groups needs
to b e cosc heduled, an application can also b e represen ted b y m ultiple group
SEs. This complicates the w eigh t calculation to retain fairness at application
lev el, b ecause load represen ted b y differen t group SEs of one application do es
not need to b e balanced; only within eac h represen ted group a balance of load is
necessary . This act of breaking up a group SE creates m ultiple split SEs , whic h
are enqueued in its stead. The w eigh t of a split SE dep ends – among other
things – on the load it represen ts. Load itself is simply the aggregation of all
w eigh ts as for the w eigh t of an aggregating SE.
Definition 21 (Load of a SE) . The load of a SE se is the sum of w eigh ts of all
runqueues r q i represen ted b y this SE.
l oad ( se ) = ∑︂
r q i ∈ se
w eig ht ( r q i )
Definition 22 (W eigh t of split SEs) . A split SE se i represen ts a subset of some
other (imaginary) SE se . Its w eigh t is the corresp onding fraction of the w eigh t
of the SE se .
w eig ht ( se i ) = w eig ht ( se ) · l oad ( se i )
l oad ( se )
Note, that the sum of w eigh ts of all split SEs se i is equal to the w eight of the
original SE se .
∑︂
se i
w eig ht ( se i ) = w eig ht ( se )
The result of splitting a group SE is – except for the hierarc hical asp ect
– similar to task groups in Lin ux. Though, the tec hnique can also b e used
to enqueue a single SE m ultiple times in differen t SD hierarc hies b y using a
predefined fraction of the w eigh t for each split SE. This is needed by one of the
fragmen tation a v oidance tec hniques describ ed in Section 6.4.
6.2 Mapping of Cosc heduled Sets
F or eac h cosc heduled set a separate set of prop erly in terconnected SDs is created.
This sub-hierarc h y is then link ed via one or more horizon tal connections to the
paren t set in the existing SD hierarc h y . The n um b er and allo w ed p ositions
6.2. MAPPING OF COSCHEDULED SETS 75
of these horizon tal links dep end on the sc heduling constrain ts that come with
the cosc heduled set in question. Time constrain ts usually limit the n um b er
of horizon tal connections or they limit horizon tal connections to SDs within a
certain size range. Placemen t constrain ts, on the other hand, usually dictate
allo w ed shap es and p ositions of SDs.
Within these constrain ts, the op erating system is free to c ho ose the in ternal
la y out of the SD hierarc h y , so that it can ac hiev e an o v erall balance and an op-
timization to w ards op eration goals more easily . In the follo wing, the restrictions
on the SD hierarc h y induced b y sc heduling constrain ts are discussed.
6.2.1 Time constrain ts
With the exception of the minimal parallelism constrain t, whic h has a somewhat
sp ecial p osition, the time constrain ts on a cosc heduled set con trol the degree of
concurrency . Within the SD hierarc h y , ev ery horizon tal connection from one
cosc heduled set to its paren t represen ts an isle, where tasks will b e executed
sim ultaneously .
Cosc heduling Constrain t. F or a cosc heduled set with a cosc heduling con-
strain t this means, that there ma y b e at most one horizon tal connection to its
paren t. T o mak e sure that indeed all runnable tasks (or nested sets) are cosc hed-
uled, the SD with the horizon tal connection m ust b e wide enough to encompass
all runnable tasks. T o reduce fragmen tation, this SD should also b e as small as
p ossible, so that there are more sc heduling opp ortunities within the paren t set.
This means, that the horizon tal link is relo cated automatically to a more suit-
able SD, when the n um b er of runnable tasks c hanges and the curren tly linked
SD is not the b est a v ailable matc h an ymore.
A relaxed v ersion of this constrain t ma y c ho ose to do this relo cation dela y ed,
esp ecially to w ards a larger SD. This reduces in ternal fragmen tation at the cost
of sometimes cosc heduling only a subset of tasks within the set.
Maximal Concurrency Constrain t. An upp er limit on the degree of con-
currency is enforced b y restricting a cosc heduled to a single SD with a width
not exceeding the allo w ed maxim um. When there are more tasks than CPUs,
time-sharing will b e used within leaf SDs to sc hedule all tasks. Note, that this
constrain t alone still allo ws to form horizon tal links at will as long as they are
b elo w or at the men tioned SD. Ha ving more than one horizon tal link cannot
guaran tee cosc heduling of all tasks anymore, but that is not the goal. Instead,
using for example leaf SDs within the set k eeps sync hronization requiremen ts –
and with it fragmen tation – to a minim um.
Unless an implemen tation supp orts arbitrarily sized SDs, not all maxim um
v alues can b e supp orted. A relaxed v ersion of this constrain t could round the
limit up w ards to the next supp orted v alue, instead of do wn w ards whic h is nec-
essary for the strict v ersion.
76 CHAPTER 6. TOPOLOGY-A W ARE COSCHEDULING
Minimal Concurrency Constrain t. A lo w er limit on the degree of concur-
rency is ac hiev ed b y not allo wing horizon tal links to form b etw een SDs with a
width b elo w the minim um. A dditionally , horizon tal links b ey ond the first ma y
only b e established, when enough tasks (or nested sets) are a v ailable to fully
o ccup y all link ed SDs. This w a y , ev en when only one subset is executed, the
constrain t is fulfilled. If there is only one link and not enough runnable tasks
to ev en reac h the lo w er limit, it mak es sense to handle this constrain t lik e a co-
sc heduling constrain t, so that SDs b elo w the limit get used instead of half-empt y
larger SDs.
Minimal P arallelism Constrain t. A minim um degree of parallelism could
b e realized b y main taining a coun ter of runnable tasks (or nested sets) within a
set. Only when enough tasks are runnable, the set itself is considered runnable
and enqueued in the paren t set. How ev er, as this constrain t is mostly used
together with the cosc heduling or minimal concurrency constrain t, an alternativ e
realization is to declare a set not runnable, once a horizon tal connection b elo w
the minim um w ould b e established. By simply not establishing this link, the set
is dequeued.
6.2.2 Placemen t constrain ts
With one exception, placemen t constrain ts do not enforce an y limit on the num-
b er of horizon tal links. Instead, placemen t constrain ts define allo w ed la y outs of
the sub-hierarc h y of SDs making up a cosc heduled set. So far, giv en examples
alw a ys orien ted themselv es at the system top ology . But this is just one w a y of
organizing SDs, whic h is sufficien t for only three of the fiv e defined placemen ts
constrain ts.
Resource Sharing Constrain t. The resource sharing constrain t is suc h a
constrain t, whic h tak es the system top ology in to accoun t. Sp ecifically , a cosc hed-
uled set ma y only b e mapp ed within a SD, whic h has all of its CPUs within a
matc hing top ological unit (TU). This results in an implicit upp er limit of con-
currency , but do es not prev en t arbitrary w a ys of structuring the SD hierarc h y
within said TU.
Isolation Constrain t. The isolation constrain t is similar to the resource shar-
ing constrain t. Though, in order to realize the asp ect of isolation, it has some
additional requiremen ts: Firstly , the target SD m ust fully encompass a to-b e-
isolated TU, so that no CPU can b e part of a differen t SD. Secondly and for the
same reason, the horizon tal link to the paren t set m ust b e at this SD, so that
no CPUs of the SD can b e used for something else. Finally , the sc heduler ma y
not p erform alternate selections within this SD as discussed later in Section 6.4.
6.3. LO AD BALANCING 77
Close Placemen t Constrain t. The close placemen t constrain t is more or
less a dynamic application of the resource sharing constrain t. The t yp e of TU
to b e used as an anc hor for a cosc heduled set with this constrain t is determined
b y the amoun t of runnable SEs, so that all SEs end up on differen t sets of
CPUs. Generally , the TU in question is just large enough to accommo date all
SEs. How ev er, a relaxed implemen tation migh t c ho ose to switc h to a larger TU
earlier than necessary , e. g., b ecause it uses some CPUs of a TU for a differen t
SD.
If sibling TUs are in terconnected with a net w ork (e. g., NUMA domains)
and only a few are needed, they should b e placed near to eac h other, so that
comm unication costs within the set are minimal.
Resource Non-sharing Constrain t. The resource non-sharing constrain t
cannot mak e use of SDs aligned to the system top ology . Instead, this constraint
need the exact opp osite: SDs crosswise to the system top ology , for example one
CPU p er pro cessor. Sp ecifically , a SD may ha v e at most one CPU p er matc hing
TU. This results in an implicit upp er limit of concurrency , but do es not ha v e
further impacts, similar to the resource sharing constrain t.
Indep enden t Placemen t Constrain t. This is a dynamic application of the
resource non-sharing constrain t, in the same manner that the close placemen t
constrain t is the dynamic v ersion of the resource sharing constraint. Th us,
the SD selection also dep ends on the n um b er of runnable SEs. A hierarc h y of
v alid SDs w ould b e aligned to an “in v erse system top ology” . T aking a t ypical
In tel system with SMT and NUMA as example, this in v erse top ology w ould
start at the system lev el, branc h out according to the n um b er of SMT siblings
p er core, then branc h out according to the n um b er or cores p er pro cessor, and
finally branc h out according to the n um b er of pro cessors or NUMA no des within
the system. As with the close placemen t constrain t, a unit within this inv erse
top ology is selected so that the resulting n um b er of CPUs is just large enough
to accommo date all SEs.
Note, that as so on as a cosc heduled set with a constrain t lik e this and another
set with, e. g., a close placemen t constrain t are nested, the resulting SDs will
b e aligned neither to the regular nor the in v erse system top ology . It will b e
something in b et w een, but follo wing the same ideas.
6.3 Load Balancing
Load balancing m ust consider the SD hierarc h y , as it determines from whic h
runqueues SEs are selected sim ultaneously and b et w een which runqueues SEs
can b e mo v ed without upsetting an y cosc heduled sets and their constrain ts. This
means, that whenev er load is mo v ed, it has to stay within the cosc heduled set
78 CHAPTER 6. TOPOLOGY-A W ARE COSCHEDULING
it came from. Ho w ev er, dep ending on the actual constrain ts, there are v arious
options to mo v e that load around.
Lo oking at a cosc heduled set, i. e., a set of SDs with v ertical links, the load
within that set comes in the form of SEs enqueued in the v arious SDs. With the
exception of SEs used to realize v ertical links, the SEs either represen t nested
cosc heduled sets or tasks. Moving load means to dequeue one or more SEs in
one SD and enqueue them in other suitable SDs. Ho w ev er, with nested sets
and m ultiple horizon tal links, dequeuing a SE and enqueuing it elsewhere in a
set, lo oks differen t in the paren t set. Also, there is the p ossibilit y to reestablish
a horizon tal link b et w een t w o cosc heduled sets at SDs of a differen t size than
b efore – essen tially c hanging the n um b er of cosc heduled CPUs in the set – whic h
has its o wn p eculiar effect on the o v erall load situation.
Ideally , a state w ould b e reac hed, where tasks receiv e their fair amoun t of
CPU time without ha ving to sh uffle them around an ymore. Phrased differen tly:
if tasks w ere k ept stationary and eac h task w ould only receiv e its fair amoun t
of CPU time, then there w ould b e no undue idle time (a. k. a. fragmen tation)
in the system. Within the SD hierarc h y this global state of b eing balanced can
b e brok en do wn: eac h SD defines an isle where its CPUs execute SEs for the
same amoun t of time. Th us, if eac h SD is balanced in itself, the whole system
is balanced. This allo ws the realization of a distributed load balancer.
A single SD is balanced, when within eac h group of v ertically link ed c hild
SDs the a v erage w eigh t p er CPU is the same for ev ery v ertically link ed c hild
SD. That is, within a v ertically link ed group, SDs of iden tical size ha v e the
same w eigh t, while a SD with t wice as m uc h CPUs as another SD is t wice as
hea vy . More formally , eac h runqueue (or SE) has a c ap acity , whic h is – in this
thesis – equiv alen t to the n um b er of CPUs represen ted b y that runqueue (or
SE). (V arying the definition of the capacit y w ould allo w handling of asymmetric
parallel systems.) Within a group of runqueues that is represen ted b y a single
SE, the load m ust b e distributed prop ortionally to that capacit y . This leads to
the follo wing definition of the targeted load for a runqueue.
Definition 23 (T arget load for a runqueue) . The target load for ev ery runqueue
r q i represen ted b y a SE se is a fraction of the total load represen ted b y the SE,
prop ortional to its capacit y .
tar g etl oad ( r q i ) = l oad ( se ) · capacity ( r q i )
capacity ( se )
If all c hild SDs are equally sized, this is a simple a v erage.
In order to reac h the targeted load for a runqueue, load m ust b e shifted ei-
ther to w ards or a w a y from it – alternativ ely one could influence other parts of
the equation, suc h influencing the o v erall load of the encompassing SE without
touc hing the runqueue in question. There are sev eral p ossibilities to ac hiev e
differen t effects and com binations thereof dep ending on what is allow ed b y con-
strain ts:
6.4. FRA GMENT A TION A V OID ANCE 79
1. Balance runqueues represen ted b y a SE.
2. Reduce or increase in ternal im balances of SEs within represented run-
queues.
3. Reduce or increase w eigh t of the SE in question.
The simplest case is a migration of a task or a group of tasks b et w een run-
queues of SDs of equal width: dequeue an SE from one runqueue and enqueue
it in another – with b oth runqueues b eing part of the group of to-b e-balanced
SDs. If the dequeued SE represen ts a part of a cosc heduled set with m ultiple
SEs, then the target runqueue may not already ha v e another SE of the set in
question. Then, this is a pure load mo v ement b et w een the t w o runqueues in
question without affecting an ything else. F rom here, there are sev eral v aria-
tions. If one or b oth of the runqueues are only reac hable b y tra v ersing the SD
hierarc h y do wn w ards (via different links), then the migration will also affect the
balance within passed SDs. Care should b e tak en, that suc h a migration do es
impro v e the o v erall situation. If a nested set has (or ma y ha v e) m ultiple SEs,
then load can b e shifted partially from one of these split SE to another – p o-
ten tially creating further split SEs or merging existing ones. If the SD width
required b y an SE is not fixed, i. e., the cosc heduled set can b e resized, then
load mo v emen t ma y also o ccur b et w een SDs of differen t widths. Dep ending on
the st yle of fairness for this set (cf. Section 6.1.3), the w eigh t of the mo v ed
SE migh t c hange: the same-CPU-time fairness is neutral; the same-system-time
fairness mak es SEs ligh ter when enqueuing them in smaller SDs. As a sp ecial
case, if load is mo v ed up- or do wn w ards the SD hierarc h y , a runqueue can b e
made ligh ter or hea vier without influencing its siblings.
The design itself has no requiremen ts for concrete algorithms for load mo v e-
men t. Though, there are some more guidelines to follo w than just simply bal-
ancing ev ery SD on its o wn. Sp ecifically , the load balancing – as it has b een
describ ed – is effectiv e only in the presence of time constrain ts. If a set has just
placemen t constrain ts, the placemen t will adhere to the constrain t, but the load
it not necessarily balanced. This ma y or ma y not b e an issue for a particular use
case. Th us, on a case b y case basis, load balancing could handle a set as if there
w as just one horizon tal link in to the paren t instead of man y , or load balancing
could a v oid to let allo w ed CPUs to go “idle” with resp ect to the set. A ddition-
ally , it will not alw a ys b e p ossible to ac hiev e a p erfect balance, whic h migh t
require some additional t w eaking of the balancing. Some options are discussed
in the follo wing t w o sections.
6.4 F ragmen tation a v oidance
The SD hierarc h y is not only used to realize the differen t sc heduling constraints,
but also to manage fragmen tation: The imp osed structure is used to enforce
80 CHAPTER 6. TOPOLOGY-A W ARE COSCHEDULING
go o d mappings and to easily find wo rk for CPUs if the curren t cosc heduled set
cannot k eep them busy .
6.4.1 Idle selection
A cosc heduled set consists of a set of in terconnected SDs. This set is assumed
to follo w a global structure – for example that of the system top ology . In that
scenario, applications hav e v ery little options in their degree of parallelism: core,
so c k et, system. When some CPUs cannot b e k ept busy , this leads to fragmen-
tation. Presen tly , in order to address this, it w ould require enough CPUs going
idle, so that the whole application can b e shifted to one of the smaller c hild SDs.
Ho w ev er, due to eac h cosc heduled set follo wing the same global structure,
this means in particular, that the paren t set has similar SDs as the curren t
set. This allo ws idle CPUs to pic k up w ork without violating any cosc heduling
guaran tees: Whenev er an SD within a group is idle, the sc heduling decision
pro ceeds within the iden tical SD in the set’s paren t. This wa y , the curren t
and the paren t group are still cosc heduled. That is, the SDs of the paren t set
are effectiv ely sc heduled with idle priority within their c hildren forming r everse
horizontal c onne ctions within the SD hierarc h y . (The extra time some SDs are
executed due to this idle selection, leads to a sk ew in fairness and their w eights
are no longer prop ortional to the receiv ed CPU time. This is addressed with a
global solution in the next section.)
T o mak e this idle selection more efficien t, care must b e tak en during load
balancing to free few er larger SDs instead of more smaller SDs. With only small
SDs, larger applications are prev en ted from b eing executed and there migh t not
b e enough small ones to fill the holes. Th us, the balancing co de m ust trac k the
n um b er runnable tasks within the SD hierarc h y , so that an informed decision can
b e made to migrate tasks from one half-filled SD to another once the com bined
load can b e sustained b y a single SDs. Something similar is actually already done
b y curren t op erating system sc hedulers – only in the con text of p o w er sa ving:
when all load fits on to one pro cessor, it can b e consolidated, so that the second
pro cessor can b e put in to a deep er sleep state, conserving energy .
6.4.2 Asymmetric SD shap es
F or cases, whic h cannot b e handled b y the idle selection alone, it is also p ossible
to, e. g., realize a 5:3 split of computational resources b y creating appropriate
SDs and let them co-exists with the “regular” SDs in a cosc heduled set. Ha v-
ing m ultiple hierarc hies a v ailable at the same time is also required to supp ort
differen t kinds of placemen t constrain ts sim ultaneously .
T o k eep the SD hierarc h y from extensiv e gro wth due to a com binatorial
explosion and to k eep load balancing and idle selection efficien t, these different
hierarc hies m ust not fan out in to their o wn leaf SDs. Instead the hierarc hies
con v erge to the same set of leaf SDs. This can either b e ac hiev ed b y enqueuing
6.5. TRA CKING OF CPU TIME 81
an SD in m ultiple paren t SDs b y splitting its load across its paren ts as describ ed
in Section 6.1.3, or b y a set-in ternal application of the idle selection itself – or a
com bination of b oth.
The difference b et w een explicitly realizing a certain split, whether it is in to
a separate hierarc h y or joining m ultiple hierarc hies again, and relying on the
idle selection, is that the former is more suited to w ards stable situations due to
setup costs, while the latter handles dynamic situations just fine but is not as
v ersatile.
6.5 T rac king of CPU time
Usually , it is imp ossible to completely a v oid all load im balances. This might
cause SEs to receiv e more or less CPU time than in tended. Esp ecially , in mo-
men ts of c hanging load, some SEs migh t gain an adv an tage un til the load bal-
ancing mec hanism is able to almost balance ev erything out again. Ev en then,
these small im balances accum ulate o v er time. Un til no w, the presen ted mec h-
anisms do not handle this. That is, a SE that – for whatever reason – gains
an adv an tage o v er other SEs, will not b e automatically p enalized in the future.
One p ossibilit y is to o ccasionally shift the im balance load to ev en ev erything out
– at least probabilistically . But this is on the one hand not that exact, and on
the other hand problematic in the hierarc hical scenario, where load can also b e
c hanged in width.
A dditionally , idle sc heduling and enqueuing on SE m ultiple times in tro duce
new conceptual im balances: some runqueues b elo w an aggregating SE get more
CPU time than others. Hence, balancing weigh t equally is no longer adequate.
While this could b e addressed b y giving runqueues differen t pro cessing capacities
and balancing load prop ortionally to this (essen tially a v arian t of asymmetric
m ultipro cessor sc heduling), the fact remains, that the amoun t of extra capacit y
is mostly just guessed.
T o address these issues altogether, it is p ossible to trac k the receiv ed CPU
time of a SE, compare it to the exp ected CPU time, and design the sc heduling
and balancing algorithms around that. (F or example, older v ersions of VMw are
do this on a p er-VM basis, cf. Section 4.4.) In this thesis, a different approach
is suggested, whic h w orks w ell together with the prop ortional-share sc heduling
and balancing in the hierarc hical setup. Simply put, the w eigh t of a SE is
dynamically adapted when it is ahead or b ehind its exp ected time. The more
b ehind a SE is, the hea vier it gets; the more ahead, the ligh ter. Within a single
runqueue this has the adv an tage, that run time differences are ev ened out slo wly .
F or example, if a SE got ahead for some reason, it will still b e executed – alb eit
a bit slo w er – instead of b eing forced to sit out un til the others catc h up (and
p ossibly causing a noticeable lac k of resp onse for the user). In the context of
m ultiple runqueues, this concept causes SEs on runqueues, whic h get less run time
than appropriate, to get hea vier o ve r time, un til the increasing w eigh t im balance
82 CHAPTER 6. TOPOLOGY-A W ARE COSCHEDULING
C P U 1 C P U 2 C P U 3 C P U 4
W eigh t
B C D E
A A
(a) W eigh t distribution.
A B C D E
0
10
20
R un time [%]
(b) Resulting CPU time distribution.
Figure 6.3: W orst case situation for a simple p erio dic rebalancing algorithm:
task A is b ounced b et w een t w o CPUs.
triggers a rebalancing b et w een runqueues. Compared to a simple p erio dic shift of
im balance without dynamic w eigh ts, this v arian t alw a ys migrates the “correct”
SEs. Another effect is, that hea vier queues are less attractiv e for new SEs.
As an example, consider fiv e similar tasks on a quad-core system. One of the
a v ailable CPUs will ha v e to execute t w o tasks, whic h then do not mak e as m uc h
progress as the other three tasks. With just a simple p erio dic rebalancing, it
could b e that alw a ys the same task is b ounced b et w een t w o CPUs, resulting in
one really slo w task, t w o sometimes slo w tasks, and tw o fast tasks as depicted
in Figure 6.3. With w eigh t adjustments, it is not necessary to supp ort p erio dic
rebalancing at all. Instead, it is sufficien t to balance tasks only , if the o v erall
balance is actually impro v ed (and the impro v emen t is ab o v e a certain threshold
to prev en t rapid re-balancing). In the giv en example, whic h is illustrated with
v alues obtained from a sim ulator run in Figure 6.4, the t w o tasks on the o v er-
loaded CPU will get hea vier o v er time (while the others get ligh ter). A p oin t
will come, where mo ving one of the hea vier tasks off its CPU mak es sense, as
the o v erall balance is impro v ed. The receiving CPU no w has t w o tasks, a hea vy
and a ligh t one. This will cause the hea vy one to catc h up to the ligh t tasks
un til their w eigh ts ev en out and eac h task on this CPU receiv es again 50% of
CPU time. While this is going on, globally b oth of these tasks will get hea vier
compared to the tasks running on the other three CPUs – again un til it mak es
sense to mo v e one of the t w o tasks to another CPU. The CPU that had t w o
tasks originally will not b e the receiving CPU this time, b ecause its task is still
hea vier compared to the tasks on the other t w o CPUs.
In the con text of the idle selection from Section 6.4.1, the dynamic w eigh t
adjustmen t causes tasks that receiv e extra CPU time to get ligh ter. A t some
p oin t, the runqueue w ould attract additional load, either during balancing or
during task creation. As a sp ecial case, when there is no additional load that
can b e pulled, the load balancer should b e able to exc hange a ligh t SE with
a hea vy SE so that another SE can profit from the additional capacit y of the
runqueue.
T o realize these dynamic w eigh t adjustmen ts, the CPU time receiv ed b y
eac h SE is trac k ed. T o mak e the times comparable b et w een SEs of differing
base w eigh ts and hierarc h y lev els, the times are normalized to the default task
6.5. TRA CKING OF CPU TIME 83
C P U 1 C P U 2 C P U 3 C P U 4
W eigh t
B C D E
A
(a) Initial situation.
C P U 1 C P U 2 C P U 3 C P U 4
W eigh t
A
B
C D E
A
(b) First balancing.
C P U 1 C P U 2 C P U 3 C P U 4
W eigh t
A
B C D E
C
(c) Second balancing.
C P U 1 C P U 2 C P U 3 C P U 4
W eigh t
A
B
C
D
E
D
(d) Third balancing.
Figure 6.4: Same situation as in Figure 6.3 with dynamic w eigh t adaptation.
Whenev er an im balance threshold is crossed, the b est fitting task is migrated
from the most to the least loaded CPU. The fourth balancing w ould mo v e E
on to C P U 1 ; w e are roughly bac k to the initial situation and the cycle rep eats.
w eigh t. That is, receiv ed CPU time is trac k ed one-to-one for tasks with the
default w eigh t, while time for a task of half that w eigh t progresses t wice has
fast. Hence, when all SEs ha v e receiv ed their prop ortional share of some time
in terv al, they will all ha v e exp erienced the same incremen t for their normalized
CPU time.
Definition 24 (Normalized SE CPU time) .
ntime ( se ) += ∆ time ( se ) · w 0 · capacity ( se )
w eig ht ( se )
By comparing individual SE times with the a v erage time of a group of SEs,
e. g., a runqueue, it is p ossible to determine whether a SE is b ehind or ahead.
Definition 25 (Normalized runqueue CPU time) . The normalized time of a
runqueue (or an y other t yp e of group) is the w eigh ted a v erage of its SEs.
ntime ( r q ) = ∑︂
se i ∈ r q
ntime ( se i ) · w eig ht ( se i )
w eig ht ( r q )
Usually , it is not necessary to calculate this v alue explicitly , b ecause it can b e
substituted b y the normalized time of the enclosing group/aggregating SE, whic h
is already a v ailable via the regular SE CPU time accoun ting.
84 CHAPTER 6. TOPOLOGY-A W ARE COSCHEDULING
Definition 26 (Dynamic SE w eigh ts) .
dy nw eig ht ( se ) = w eig ht ( se ) · 2 f · ( ntime ( r q ) − ntime ( se ))
Dynamic w eigh ts mak e a SE hea vier or ligh ter dep ending on whether it is
b ehind or ahead, resp ectiv ely . The larger the difference to the target time, the
more the dynamic w eigh t will deviate from the original w eigh t. The aggres-
siv eness of the w eigh t adjustmen t can b e con trolled with the parameter f . F or
example, when set to 1
60 s it means that a tasks that deviates b y a min ute of nor-
malized run time from the a v erage, will execute t wice (or half ) as fast as usual to
mak e up for it. The un b ounded gro wth ensures an ev en tual rebalancing in cases,
where a small c hange is not able to shift the im balance for the necessary amoun t.
If the dynamic w eigh t go es to w ards zero, the SE mak es to o m uc h progress and
it indicates that it migh t b e necessary to artificially stop the SE for a momen t.
These dynamic w eigh ts are used to calculate the share of a SE and for load
balancing, but not for the actual accoun ting of receiv ed CPU time. The giv en
definition has the adv an tage, that sev eral actions within the SD hierarc h y can
b e short-circuited, b ecause 2 c − b · 2 b − a = 2 c − a . F or example, for load balancing
within a cosc heduled set made up of a m ulti-lev el SD hierarc h y , dynamic w eigh ts
of individual SEs can b e deriv ed directly , without ha ving to stop at in termediate
lev els to determine their con tribution to the dynamic weigh t. T o prev en t new
SEs or SEs w ok en after a long sleep from “catc hing up” for extended p erio ds of
time, their normalized time is initialized (or reinitialized) appropriately . Newly
fork ed tasks can b e initialized with the time of the curren tly running task. W ok en
tasks k eep their normalized time, unless it is considered to o far b ehind in whic h
case it is forw arded to a more considerate time. If it is clear, whic h other task
caused the w ak eup, it is also p ossible to reflect the causalit y b y cop ying the
corresp onding time if it is larger.
6.6 Chapter Notes
A less flexible v ersion of the basic structure in Section 6.1 w as previously pub-
lished b y m yself in [84]: SDs w ere fixed at the time, the SD hierarc h y homoge-
neous, and fairness across hierarc h y lev els had not b een giv en that m uc h though t.
In a sense, this publication has b een the pro of-of-concept that pursuing a thesis
in this direction is w orth while.
Chapter 7
In tegrating T A CO
After the presen tation of the cosc heduler design in previous c hapter, this c hapter
fo cuses on the application of said design. A metho d is describ ed, whic h con v erts
an existing “regular” m ulticore sc heduler in to a sc heduler capable of cosc hedul-
ing. Beha vior and features of the existing sc heduler are preserv ed, enabling a
smo oth transition to w ards cosc heduling-enabled systems.
This c hapter starts with the requiremen ts for a successful conv ersion in Sec-
tion 7.1 and a description of the con v ersion metho d itself in Section 7.2. Af-
terw ards the metho d is applied to Lin ux in Section 7.3, whic h results in the
cosc heduler that is used throughout the remaining thesis. The c hapter closes
with a study of F reeBSD in Section 7.4 and ho w the metho d w ould b e applied
there.
7.1 Requiremen ts for In tegration
In order to add cosc heduling functionalit y to existing schedulers while preserving
their existing set of features, an existing sc heduler is dissected in to its building
blo c ks. These building blo c ks are then rearranged and put bac k together. De-
p ending on the original sc heduler and the desired cosc heduling features, this
pro cess can b e more or less complex. In particular, it is a v ery sc heduler-sp ecific
pro cess.
The sc heduler in question needs to fulfill certain requiremen ts, so that the
pro cess can b e applied easily . Throughout this thesis, this is collo quially ex-
pressed b y requiring a gener al purp ose multic or e sche duler as a base to w ork
from, whic h not only con v eys an idea of the targeted application domain but also
sets some exp ectations on sc heduler prop erties, that can b e tak en for gran ted.
The term “m ulticore” indicates the need for a parallel system and a corresp ond-
ing sc heduler; on non-parallel systems there is no cosc heduling p ossible. T ec h-
nically , the system do es not ha v e to use (just) m ultiple cores to ac hiev e par-
allelism, but it curren tly the most widespread metho d. The “general purp ose”
part implies an absence of structural sp ecialization one migh t find in single pur-
p ose sc hedulers, suc h as fixed pro cess lists or an absence of preemption in some
em b edded/real-time systems.
85
86 CHAPTER 7. INTEGRA TING T A CO
The original sc heduler is considered to b e assem bled from the follo wing build-
ing blo c ks:
• sche duling entities , whic h represen t either tasks or groups of tasks;
• runqueues , whic h hold sc heduling en tities;
• functions op er ating on a single runqueue , suc h as enqueuing and dequeuing
sc heduling en tities and determining the next scheduling en tit y to execute;
• functions op er ating on multiple runqueues , suc h as load balancing functions
and task placemen t.
In addition to these structural requiremen ts, the original scheduler has to
fulfill t w o additional requiremen ts, so that adequate cosc heduling can b e or-
c hestrated: Firstly , the original sc heduler m ust supp ort preemption. Without
preemption there w ould b e no mean to enforce an y kind of useful cosc heduling.
Secondly , the original sc heduler should supp ort some kind of w eigh t prop ortional
sc heduling. Otherwise it will not b e p ossible to k eep CPU time ratios of the v ar-
ious tasks iden tical to those of the original sc heduler – or only in a v ery limited
fashion.
There are no other limitations on the design of the original sc heduler. Ho w-
ev er, b ecause all building blo c ks are reused during the con v ersion, dra wbac ks
of the original sc heduler will resurface in the con v erted sc heduler. F or example,
it do es not matter for the con v ersion, whether the original sc heduler utilizes
a global runqueue or distributed runqueues in some w a y , lik e one runqueue
p er CPU. In case of a global runqueue, the resulting sc heduler will still utilize
global runqueues; it will not suddenly b ecome scalable. Similarly , if the original
sc heduling algorithm is susceptible to certain attac ks, they will still w ork after
the con v ersion.
7.2 Con v ersion of Non-cosc heduling Sc hedulers
The pro cess of augmen ting a sc heduler with supp ort for top ology-a w are co-
sc heduling can b e split in to fiv e consecutiv e steps. Eac h step has to b e applied
with the sp ecific sc heduler in mind that is b eing con v erted. There is no one-fits-
all implemen tation. After eac h step, the sc heduler is still fully op erational and
retains its prop erties. A t the end, cosc heduling is supp orted. Note, that some
lo c king and p erformance asp ects are discussed only afterw ards.
Step 1: Supp ort a task grouping mec hanism. Cosc heduled sets are
groups of tasks. Hence, infrastructure is needed to manage groups of tasks.
Ideally , the op erating system already supp orts a con tainer mec hanism that is
generic enough. Note, that the one con tainer for tasks, which is lik ely to exist
in an op erating system – namely the concept of a pro cess – do es not pro vide
7.2. CONVERSION OF NON-COSCHEDULING SCHEDULERS 87
the necessary flexibilit y: it is to o tigh tly coupled to address spaces to allo w a
wide v ariet y of cosc heduling use cases. F or full flexibilit y , a con tainer concept is
needed that is orthogonal to pro cesses.
Step 2: R unqueue generalization. If not already supp orted, the runqueue
data structure is generalized, so that runqueues can b e allo cated and freed on
demand (for later dynamic creation of cosc heduled sets) and are able to hold
differen t t yp es of sche duling entities – the basic t yp e of a sc heduling en tit y
b eing a task. In case the runqueues are protected b y lo c ks, the lo c ks need to b e
decoupled from the runqueue data structure itself.
Step 3: Hierarc hical organization of runqueues. The runqueues are re-
organized hierarc hically . The “normal” runqueues for tasks b ecome the b ottom
la y er, and runqueues represen ting subsets of these runqueues are added on top –
eac h runqueue represen ting a sync hronization domain. A new t yp e of sc heduling
en tit y is added – the synchr onization domain SE (SD-SE) – whic h represen ts
the runqueues b elo w it in the hierarc h y . T ask enqueuing/dequeuing and task
selection are adapted to w ork along the hierarc h y . If desired, it is p ossible to
apply the hierarc hical reorganization to only a subset of the sc heduling class-
es/priorities supp orted b y the op erating system. This allo ws, for example, to
handle k ernel activities outside the scop e of the cosc heduler.
Step 4: A daptation of functions op erating on m ultiple runqueues.
With cosc heduled sets there will b e m ultiple sets of runqueues. The load bal-
ancing mec hanism is adapted, so that it is able to w ork within only a certain
set of runqueues. This prev en ts tasks from switc hing b et w een sets unexp ect-
edly . Similar adaptations are done for other functions that op erate on m ultiple
runqueues, suc h as the selection of a runqueue for newly created or w ok en tasks.
Step 5: Supp ort for cosc heduled sets. Infrastructure to create and destro y
additional sets of hierarc hical runqueues is added – eac h additional set represen ts
a cosc heduled set. This is coupled to the task group mechanism. Knobs are
added to b e able to configure the b ehavio r of the cosc heduled set. While some
of these will b e cosc heduling-sp ecific, others will resemble asp ects that w ere
previously only a v ailable for tasks but apply to cosc heduled sets as w ell – w eigh t
and CPU affinit y for example.
The load balancer is not only applied to the runqueues in the ro ot set, but to
eac h cosc heduled set individually . F or on-demand in v o cations, lik e idle or w ak e-
up balancing, this is a straight-forw ard application. F or p erio dic in v o cations,
one option is to k eep trac k of the p erio d p er set and base the p erio d on CPU
time instead of w all-clo c k time. This w ould a v oid balancing sets, whic h are
curren tly not running an yw a y , while not deviating from the b ehavior of the
original balancer.
88 CHAPTER 7. INTEGRA TING T A CO
A t this p oin t, the newly in tegrated cosc heduling functionalit y is already fully
functional. But some things can still b e impro v ed to w ork b etter with the newly
in tro duced hierarc hical runqueue organization. F or example, one could apply
the original load balancer on eac h lev el of runqueues within a cosc heduled set.
Ho w ev er, this w ould not b e able to address im balances b et w een differen t lev els,
where – for example – one half of the b ottom lev el runqueues ha v e to shoulder
a higher load, so that they offset the extra load caused b y small cosc heduled
sets on the other half. If the original load balancer supp orts asymmetric load
distributions, one could address this b y propagating the remaining im balance of
one lev el do wn to the next lev el. (Note, that idle balancing will also tak e care of
some scenarios without doing an ything sp ecial.) This lea v es only load balancing
b y resizing of cosc heduled sets, for whic h there is nothing similar in traditional
sc hedulers.
The hierarc hical organization of runqueues requires careful though t to av oid
deadlo c ks in the lo c king sc heme, if the original runqueues use fine-grained lo c k-
ing, e. g., one lo c k p er runqueue. Applying a m ulti-gran ularit y lo c king sc heme
w ould solv e the issue without to o m uc h though t. Ho w ev er, it comes with ad-
ditional o v erhead on ev ery op eration on an y runqueue. Instead, it is p ossi-
ble to sta y close to the original lo c king sc heme, grabbing lo ck s only for those
runqueues/lev els that are needed for the curren t op eration. F or this, the p er-
runqueue lo c ks are replaced with p er-SD lo c ks with all runqueues for a particu-
lar SD referencing that lo c k. Additionally , lo w er-lev el lo c ks are alw a ys acquired
b efore those on higher lev els. This w a y , one of the necessary conditions for
a deadlo c k, namely circular w ait, is a v oided. This w orks for enqueuing and
dequeuing, whic h can bubble up w ards in the hierarc h y via lo c k c haining, and
for the sc heduling decision, for whic h the master has to get all lo c ks up to its
p ersonal ro ot b efore it starts pic king sc heduling en tities do wn w ards within the
hierarc h y . If load balancing (or something else) requires to get m ultiple lo c ks
within the same lev el of the hierarc h y , the solution of the base op erating system
is reused. F or example, Lin ux acquires them b y ascending memory addresses.
Another solution – whic h is also required when the base op erating system uses
lo c k-free runqueues – is to not require atomicit y on certain runqueue op erations,
so that it is not necessary to hold m ultiple lo c ks at the same time and runqueues
can b e handled one at a time. But that means that, e. g., task pic king ma y
tra v erse do wn w ards in to a cosc heduled set, whic h gets dequeued concurren tly .
The emplo y ed algorithms m ust b e able to handle this and other cases. Hybrid
approac hes are also p ossible, where for example dequeuing is done lazily . That is,
dequeuing nev er bubbles the hierarc h y up w ards. Instead, the actual dequeuing
is done when empt y cosc heduling entities are encoun tered during task selection.
7.3. COSCHEDULING IN LINUX 89
stop deadline realtime fair idle
Figure 7.1: Scheduling classes in Lin ux. They are pro cessed in order when
selecting a task: the first task found is executed.
7.3 Cosc heduling in Lin ux
T o demonstrate the feasibilit y of the prop osed cosc heduler design and in tegration
metho d, an in tegration in to the Lin ux Completely F air Sc heduler (CFS) [83] w as
done. The result – a Lin ux 3.8 k ernel with a CFS capable of cosc heduling – is
ev aluated and used in later c hapters. This section describ es its non-in trusiv e
in tegration. This starts with an in tro duction in to the building blo c ks of the
Lin ux sc heduler in Section 7.3.1 and contin ues with their reutilization to realize
cosc heduling in Section 7.3.2. Section 7.3.3 co v ers the adaptation of the load
balancer separately . Finally , Section 7.3.4 discusses a few decisions done during
dev elopmen t and outline a p oten tial future direction.
7.3.1 Lin ux Sc heduler Basics
The last o v erhaul of the Linux sc heduler happ ened with Lin ux 2.6.23 in late
2007. Since then, the Lin ux sc heduler subsystem is somewhat mo dular with
statically prioritized sc heduling classes and p er-CPU runqueues. T ec hnically ,
there are curren tly fiv e sc heduling classes (in decreasing priorit y): stop, deadline
(since Lin ux 3.14), realtime, fair, and idle (Fig. 7.1). The stop class is only used
in ternally to realize, e. g., activ e balancing. The deadline and realtime classes are
for tasks with sp ecial needs regarding CPU time that are executed b efore normal
tasks – these t w o classes are considered out of scop e for this implemen tation.
Normal tasks are placed in the fair class, whic h is handled b y the Completely
F air Sc heduler. Only when there is no task to execute, the idle task in the idle
class is run.
The Completely F air Sc heduler (CFS) has its name for its exact accoun ting of
CPU time that do es not use the concept of (fixed) time slices. Instead, for ev ery
task a virtual run time is trac k ed and up dated whenev er that task is executed.
The runqueue on eac h CPU is a red blac k tree that is ordered b y the tasks’ virtual
run time. An ytime a sc heduling decision has to b e done, the leftmost task (with
the smallest virtual run time) is selected. This prev en ts individual tasks from
getting an unfair adv an tage o v er other tasks. New tasks get a “curren t” virtual
run time assigned; sleeping tasks keep their virtual run time to some extend. This
w a y , I/O-in tensiv e tasks are usually leftmost enough within the tree after b eing
w ok en to facilitate a preemption of the curren tly executing task. After b eing
selected eac h task is allo w ed to execute for some time unless something more
imp ortan t comes up. This “time slice” is calculated dynamically and dep ends on
sev eral factors – among them curren t load, imp ortance, and the n um b er of CPUs
in the system. CFS do es not use absolute priorities (that is what the realtime
90 CHAPTER 7. INTEGRA TING T A CO
t 1 t 2 t 3
(a) CFS runqueue with tasks ordered b y
their virtual run time, t 2 has t wice the
w eigh t of t 1 and t 3 .
t 1 t 2 t 3
(b) Resulting sc hedule, rep eating
itself un til a c hange o ccurs.
Figure 7.2: Sc heduling with CFS. T asks receiv e CPU time prop ortional to their
w eigh t. Execution time (scaled according to w eigh t) incremen ts virtual runtime;
task with least virtual run time gets executed.
class is for). Instead CFS realizes a prop ortional share sc heduling, where each
task gets CPU time prop ortionally to its w eigh t. That is, for more imp ortant
tasks (virtual run-)time progresses slo w er compared to other tasks. The w eigh t
is con trolled via nice-v alues.
This is illustrated for a single CPU in Fig. 7.2. Figure 7.2a sho ws a CFS
runqueue with three tasks ordered b y their virtual run time, the enqueue dot
represen ting their w eigh t. The leftmost task is t 1 , hence it will b e executed first.
After b eing executed for some time, it is reinserted into the runqueue, usually
to the righ t, and execution con tinues with the next task. T ask t 2 w eigh ts t wice
as m uc h as the other tasks, so it is executed t wice as long. Ho w ever, since
its virtual time is slo w ed down b y a factor of t w o due to the w eigh t, the virtual
run time of t 2 is incremen ted b y the same amoun t as the other tasks. So unless the
o v erall situation c hanges, w e see a sc hedule similar to the one shown in Fig. 7.2b.
When a sleeping task, sa y t 4 , is w ok en while t 1 is running, t 4 is inserted in to
the runqueue and a preemption c hec k is done b y comparing its p osition to the
up dated p osition of t 1 . If t 4 is enough to w ards the left, a preemption is triggered.
Ho w ev er, whic h task is executed next also dep ends on the relativ e placemen t of
t 4 to t 2 in this case. (All c hec ks in v olv e some thresholds to a v oid unfa v orable
switc hes.)
With Lin ux 2.6.24 a generic mec hanism to handle groups of tasks w as in te-
grated. These c ontr ol gr oups (c gr oups) are giv en purp ose b y attac hing one or
more cgroup subsystems to them. The sc heduler brings it o wn cgroup subsystem,
whic h allo ws realizing fairness not b et w een individual tasks but b et w een groups
of tasks. This is done b y represen ting the group b y a sche duling entity (SE)
with its o wn user-con trolled weigh t and virtual run time. This SE is enqueued
in the runqueue in place of the tasks, when there is at least one runnable task.
T o further ensure fairness b et w een tasks in a group, these tasks are managed
in a runqueue of their o wn. This structure also allows arbitrary nesting of task
groups. Figure 7.3 sho ws an example of nested task groups on a unipro cessor
system. All task groups ha v e the default w eight, whic h giv es a task group the
same imp ortance as a task. Thus, eac h of the three SEs in the system runqueue
7.3. COSCHEDULING IN LINUX 91
t 1
t 2 t 3
t 4 t 5 t 6 t 7
A
B
C
RQ
Figure 7.3: Nesting of task groups. No matter ho w m uc h w eigh t is accum ulated,
the paren t only sees a fixed w eigh t for the whole group.
will receiv e one third of the a v ailable CPU time o v erall. Within task group A ,
CPU time will b e distributed fair b et w een t 2 and t 3 , i. e., eac h gets one sixth of
the o v erall CPU time. This requires also a mo dified task selection algorithm:
Starting from the system runqueue, the sc heduler no w rep eatedly selects the
leftmost en tit y of a runqueue until it finds a task, whic h is then executed. When
necessary , e. g., when the calculated time slice for the task has expired, execu-
tion time for no w gets accoun ted at all SEs along the previously tra v ersed path.
The next sc heduling decision is again started from the top. In Fig. 7.3 task t 2
is selected first via task group A . After finishing its slice, it is reinserted righ t
of t 3 . A dditionally , the SE of task group A is up dated and mo v ed accordingly –
probably somewhere to the righ t of t 1 , ma yb e ev en righ t of the SE of task group
B . Th us, the next task is t 1 follo w ed b y either t 3 or t 4 , dep ending on the actual
v alues.
Handling of task groups in the m ulticore case is a bit more in volv ed, b ecause
of the p er-CPU runqueues. T ask groups follo w this design decision and ha v e
one runqueue p er CPU allo wing tasks of a group to b e distributed o v er m ultiple
CPUs. This also requires m ultiple represen tativ e SEs instead of just one. Lin ux
solv es the issue of fairness b y distributing the user-con trolled w eigh t of a task
group b et w een all of its represen tativ e SEs. This splitting is done prop ortionally
to the load realized b y the task group on the corresp onding CPU, so that eac h
task within the task group still receiv es its fair share no matter the actual distri-
bution of tasks. Figure 7.4 sho ws a task group in a quad-core system with four
tasks of equal w eigh t. The load distribution within the group is 3:1:0:0. The
task group itself has same w eigh t as task t 1 , whic h is split b et w een the t w o SEs
represen ting the non-empt y queues of the task group. With four tasks in the
group, eac h task should receiv e one quarter of the CPU time t 1 receiv es. Th us,
the SE represen ting the queue with three tasks get three times the w eigh t of the
other SE. Of course, if w e w ould ha v e this situation in a real system, the load
92 CHAPTER 7. INTEGRA TING T A CO
t 1
t 2 t 3 t 4 t 5
A
RQ
Figure 7.4: T ask group handling on a quad-core system. Eac h CPU has its o wn
set of runqueues: one system runqueue and one p er task group. The w eigh t of a
task group is fixed and is distributed across its SEs reflecting the in ternal load
distribution.
balancer w ould do something here. Ho w ev er, the balancer ignores task group
mem b ership and only striv es to balance load within the system runqueues. In
this example, this would mean to migrate t 2 , t 3 , or t 4 to w ards the idle CPU,
whic h already yields the b est p ossible result – still giving eac h task more CPU
time than strictly necessary .
The load balancer tries to distribute and redistribute w ork ev enly across the
CPUs. F or scalabilit y , balancing b et w een ph ysically close CPUs is done more
often than b et w een CPUs further apart. In ternally , this is realized b y defining –
p er CPU – a set of successiv ely larger system partitions called sche duling domains
(SDs) . Eac h SD is partitioned in to sche duling gr oups (SGs) . These SDs reflect
the system top ology and with the exception of large NUMA systems, eac h SD
is used as an SG in the next larger SD. (On systems with man y NUMA no des
a distance metric is used to create SDs consisting of increasing n um b ers of close
NUMA no des). Thus, on a t ypical m ulticore system there is a SD a) for a core
(in case of SMT), b) for a pro cessor, and c) for the system. Figure 7.5 sho ws this
for a dual quad-core system, whic h is used later in further examples. Except for
NUMA lev el SDs, the higher lev el SDs are shared b et w een participating CPUs.
The load balancer alw a ys w orks with an SD as input; with smaller ones
getting pro cessed more often. Within an SD, the balancer tries to ac hiev e a
balance b et w een SGs, so that all SGs ha v e the same load. The load distribution
within a SG is ignored, as this will b e addressed when the corresp onding SD is
pro cessed at another time. The balancers starts b y collecting and aggregating
load information from eac h runqueue within a SG. When there is an im balance
b et w een SGs, the balancer tries to migrate an adequate amoun t of tasks from
the busiest SG to the least loaded SG, so that the o v erall situation is impro v ed.
As a first guess, the busiest runqueue within the busiest SG is selected as source
and the idlest runqueue within the least loaded SG as destination. Though,
there is logic to handle cases, when tasks can not b e migrated for some reason
7.3. COSCHEDULING IN LINUX 93
Pro cessor 1 Pro cessor 2
System
Figure 7.5: Scheduling domains on a dual quad-core system. The load balancer
striv es to ac hiev e balance b et w een the indicated sc heduling groups b y migrating
load from one group to another.
(e. g., pinned tasks), or when enough capacit y is a v ailable so that, for instance,
a whole pro cessor can b e put into sleep mode. The balancer only w orks with
tasks. In case of task groups, the balancer considers these tasks on base of their
hierarc hical load. Going bac k to Fig. 7.4, the balancer would consider t 2 to t 5
to ha v e a quarter of the w eigh t of t 1 , eac h.
Ov er time, this ac hiev es a system-wide balance. Ho w ev er, transien t im bal-
ances ma y exist and some im balance is ev en allo w ed to cut do wn the n um b er of
migrations. Th us, some tasks ma y receiv e more CPU time compared to others
than indicated b y their w eight. While large im balances are coun tered b y p erio dic
rebalancing (e. g., three tasks on a dual core) to reduce this effect probabilisti-
cally , it is not explicitly trac k ed. In addition to this regular b ehavior, limited
balancing is also done on demand, e. g., when a CPU is ab out to go idle or a task
is ab out to b e created or (in some cases) w ok en up. This w a y , some im balances
are not created in the first place.
7.3.2 Sc heduled T ask Groups
The existing task group mec hanism pro vides an ideal base for cosc heduled sets,
b ecause they already realize a part of the accoun ting that is essen tial for man y
use cases: fairness at application lev el. A dditionally , the surrounding cgroup
system already allo ws nesting and includes infrastructure to easily set up config-
uration knobs necessary to configure cosc heduled sets. With these mec hanisms
already in place almost no additional w ork needs to b e done for Step 1 (task
grouping) and Step 2 (runqueue generalization) of the con v ersion metho d. Only
lo c king and unlo c king of runqueues is abstracted, so that lo c k-accesses are no
longer hard-co ded. F or the other steps, more w ork is necessary .
The load balancing in Lin ux is tigh tly b ound to the sc heduling domains,
whic h already pro vide a w a y to restrict balancing to a subset of CPUs (but not
y et to individual task groups). Therefore, aligning the hierarc h y of runqueues
with the hierarc h y of sc heduling domains fits the existing load balancer rather
nicely . Differen tly structured hierarc hies can then b e ac hiev ed b y influencing
94 CHAPTER 7. INTEGRA TING T A CO
+
+ +
Figure 7.6: Empt y runqueue hierarc h y on a dual quad-core system aligned with
sc heduling domains. F or ev ery non-leaf runqueue a SD-SE exists, whic h repre-
sen ts the group of runqueues b elo w it.
the generation of sc heduling domains. Only the feature of ha ving individual
sc heduling domains p er NUMA domain needs to b e deactiv ated.
Allo cating an additional runqueue for eac h sc heduling domain creates the
base runqueue hierarc h y . F or eac h new runqueue, there is also a new sc heduling
en tit y represen ting the sc heduling group b elo w it. This synchr onization domain
SE (SD-SE) is enqueued, when at least one of the represen ted runqueues has
load. In that, it follo ws the already existing logic for enqueuing and dequeuing of
task group SE, just with a logical OR. This base hierarc h y is depicted in Fig. 7.6
for a dual quad-core system, where three additional runqueues are created: tw o
for the pro cessors and one for the whole system. T ogether with this hierarc h y ,
the mo dified task selection logic is in tro duced. Where eac h CPU started at its
o wn system runqueue b efore, they no w start their selection pro cess at a runqueue
that has to b e explicitly sp ecified. F or one CPU this is preinitialized with the top
runqueue in the base hierarc h y . Whenever a CPU pic ks a SD-SE, it con tin ues
the selection pro cess in the next smaller SD, whic h con tains the CPU itself. F or
ev ery other SD represen ted b y the SD-SE, one CPU is selected and is notified
via an IPI to b egin a task selection of its o wn starting at the SD’s runqueue.
This concludes Step 3 (runqueue hierarc h y) of the con v ersion metho d. (Note,
that Step 4 (load balancing) is co v ered separately in Section 7.3.3.)
F or Step 5 (cosc heduled sets) of the con v ersion metho d, the already existing
task groups are extended similarly to Step 3 with hierarc hical runqueues and
sc heduling en tities. T ogether with the SE that comes automatically with eac h
runqueue in a task group, eac h runqueue can no w b e represen ted b y one of t w o
sc heduling en tities: via the SD-SE in the paren t runqueue, or via the original
task group SE (TG-SE) in the corresp onding runqueue in the paren t task group.
Dep ending on ho w a task group is configured, one or the other SE is used. Be-
sides resulting in the abilit y to realize cosc heduled sets of differen t sizes, it also
allo ws retaining the original task group functionalit y of group based accoun ting.
W e differen tiate b et w een r e gular task gr oups (R TGs) and sche dule d task gr oups
(STGs) . The former do not mak e use of in tra-group SEs: an y nonempt y run-
queue is represen ted separately and sc heduled indep enden tly . Sc heduled task
7.3. COSCHEDULING IN LINUX 95
groups usually ha v e exactly one link in to the paren t task group, presen ting a
united fron t to the paren t group. This difference is illustrated in Fig. 7.7.
While it ma y seem a bit excessiv e to create a whole set of runqueues ev en
for small cosc heduled sets, it is necessary for dynamic size reconfigurations to b e
able to finish without memory allo cations. A dditionally , it is curren tly necessary
to allo w migrations of whole cosc heduled sets, whic h ha v e to b e done task b y
task as Lin ux is not prepared to suddenly use a runqueue on another CPU than
it w as created for. Due to this, some hybrid forms of task groups ma y briefly
o ccur that are neither R TGs nor STGs.
7.3.3 Load Balancing
F or Step 4 (load balancing) of the con v ersion metho d the load balancer is adapted
to w ork with the runqueue hierarc h y . The previous “balancing of sc heduling
groups within a sc heduling domain” no w translates to “balancing of runqueues
represen ted b y a SD-SE” . Instead of gathering statistics o v er a bunc h of run-
queues, statistics ha v e no w to b e gathered for a subtree in the hierarc h y . This
actually simplifies the pro cess a bit, as certain needed statistics are automatically
gathered b y the hierarc hical runqueues. The handling of regular task groups in
the hierarc hical case still w orks the same w a y as b efore. The only problem here is
that the load balancer has actually to b e prev en ted from lo oking in to sc heduled
tasks groups, as these ha v e to b e migrated en-blo c. Just migrating a single task
w ould destro y the STG structure. Instead, the con ten ts of a STG are balanced
separately (the base hierarc h y is also a STG).
The indep enden t balancing of differen t STGs is p ossible, as all CPUs used
b y a STG op erate sim ultaneously and, th us, get the same amoun t of CPU time.
This indep endence of STGs mak es it p ossible to only balance STGs that are
curren tly activ e, whic h k eeps the o v erhead do wn. Note, that the activ e STG
do es not only dep end on what is curren tly executed but also on the hierarc h y
lev el that is curren tly sub ject to load balancing. F or example, consider a system
executing a lot of small STGs; when balancing at a lo w hierarc h y lev el, this will
balance within the curren tly executed STG, while balancing at a higher hierarc h y
lev el will balance the small STGs themselv es. When each STG is balanced, the
whole system is balanced.
Note, that the correction of certain im balances within an STG w as not ad-
dressed. F or example, if a cosc heduled set do es not o ccup y all CPUs of its
sync hronization domain, it w ould b e b eneficial to free a partition as large as
p ossible. Though it w as not done, it is p ossible to use the already existing bal-
ancing mec hanism for energy sa vings, whic h k eeps whole pro cessors idle if other
pro cessors ha v e enough capacit y to execute all tasks. Similarly , imbalances in
lo w er lev els of m ulti-lev el STGs ma y remain undetected as long as they a v erage
out on higher lev els.
96 CHAPTER 7. INTEGRA TING T A CO
+
+ +
t t t
+
+ +
t t t t
Sc heduled task group
Ro ot task group
(a) Sc heduled task group o ccup ying one pro cessor: only a single runqueue is en-
queued in the paren t. If the STG w ere to co ver the whole system, the link to the
paren t w ould b e b et w een the top runqueues instead.
+
+ +
t t t
+
+ +
t t t t
Regular task group
Ro ot task group
(b) Regular task group: eac h non-empt y runqueue is enqueued individually in the
paren t. If the regular task group had a sc heduled task group as a child, w e w ould
see an additional link from the R TG into its parent.
Figure 7.7: Sc heduled vs. regular task group. The same task group enqueued in
differen t w a ys in to the paren t task group.
7.3. COSCHEDULING IN LINUX 97
7.3.4 Curren t State
The curren t implemen tation has still a few deficiencies with resp ect to the re-
alization of the full v ersatilit y of the design as w ell as some concurrency issues.
Both stem from the fact that co de reuse w as considered more imp ortan t than
realizing a p erfect implemen tation. This w a y , the implemen tation demonstrates
the maxim um b enefits with the least amoun t of c hanges. It is currently at a p oint
where it can b e published to the broader comm unit y without b eing immediately
dismissed for b eing a to o big c hange or b eing of no use.
The “missing” c hanges w ould ha v e to b e in tro duced later, as their b enefit
p er c hanged line ratio is rather bad. Though some of these mo difications w ould
impro v e Lin ux in general and are not solely for the b enefit of cosc heduling.
In no particular order, these missing mo difications/features are:
• Lazy migrations. Curren tly , migrating whole task groups is rather costly
in terms of lo c king. A dditionally , it has to b e done task b y task. If
migrations could b e carried out lazily , i. e., c hanging the CPU of a task
without ha ving care whether it is curren tly running or maybe even where
it is enqueued, this w ould allo w mo ving tasks concurren tly and ma yb e
ev en en-blo c. F or v anilla Lin ux, the balancer w ould op erate more in the
bac kground than it curren tly do es. Also, the additional logic for activ e
balancing w ould b e greatly simplified.
• F urther extension and simplification of balancing co de. The run-
queue hierarc h y already holds some of the statistics that are required dur-
ing load balancing and normally only gathered on demand in v anilla Lin ux.
Consolidating these statistics further w ould allo w balancing without hav-
ing to tra v erse ev erything first – alb eit at sligh tly increased costs during
normal op erations. Also, some more statistics are needed to b e able to
address the remaining conceptual im balances.
• Non-blo c king enqueue c hec ks and lazy dequeuing. Enqueuing and
dequeuing ha v e to mo v e up w ards in the hierarc h y un til they hit a SEs
that is already enqueued b ecause of load in a sibling. Curren tly , this last
lev el has to b e lo c k ed to do the c hec k. A non-blo cking c hec k to pro vide a
fast path for the common case w ould remo ve one source of lo c k con ten tion.
Another option in this area w ould b e to do dequeues lazily without w alking
the hierarc h y at all. Instead, empt y in termediate runqueues w ould b e
dequeued when they are encoun tered during task selection.
• Non-blo c king preemption c hec ks. Curren tly , preemption chec ks in
Lin ux mo dify the runqueue ev en when the result is negativ e. Th us, re-
quiring getting the lo c k for a runqueue. This is the other source of lo c k
con ten tion, that could b e addressed.
98 CHAPTER 7. INTEGRA TING T A CO
• Generalized sc heduling domain co de. While the sc heduling domain
hierarc h y can already b e c hanged during run time, it is not in tended to
b e done often. Also, ha ving m ultiple hierarc hies at the same time is cur-
ren tly not p ossible. Without extensiv ely touc hing that co de, the curren t
cosc heduling implemen tation is limited to a single hierarc h y .
As it is, the curren t implemen tation extends the existing sc heduler b y nearly
2,000 lines of co de, whic h is an increase of ab out 10% in the sc heduling subsys-
tem. These 2,000 lines are roughly distributed as follo ws: 20% are commen ts,
50% are new functions and data structures, and 30% are additional co de in
already existing functions. All ma jor co de paths within the sc heduler remain
in tact, retaining existing prop erties. Less than 100 lines of previously existing
sc heduler co de w ere actually c hanged (i. e., less than 0.5% of the sc heduling
subsystem), making cosc heduling in Lin ux an additional feature.
7.4 Cosc heduling in F reeBSD
F reeBSD is an Op en Source op erating system that is used in a wide range of
systems. F reeBSD is used in this thesis to demonstrate the applicabilit y of
the recip e giv en in Section 7.2 to other op erating systems than Lin ux. The
T A CO concept has not b een implemen ted, but the F reeBSD sc heduler source
co de w as studied to assess the feasibilit y . The inner w orkings of the curren t
F reeBSD sc heduler are summarized in Section 7.4.1. An outline of a p ossible
in tegration of T A CO in F reeBSD is then given in Section 7.4.2, follo w ed b y a
small comparison with the Lin ux in tegration in Section 7.4.3.
7.4.1 F reeBSD Sc heduler Basics
The F reeBSD sc heduler has undergone quite a few c hanges in the recen t y ears.
Starting with a sc heduler similar to the one in UNIX, its curren t state only
sligh tly resem bles that. Esp ecially , the sc heduling of normal application tasks
has b een thoroughly c hanged. There is not m uc h do cumen tation of the cur-
ren t w orkings of the F reeBSD sc heduler. The latest publication [85] describ es
the v ersion 1.0 of the so called ULE sc heduler. It is curren tly at v ersion 3.0
with some c hanges on top, and the only do cumentation – b esides the source
co de itself whic h do es not include motiv ations – is the blog of its dev elop er Jeff
Rob erson [86] and the commit logs of the F reeBSD source rep ository [87]. The
follo wing is based on a conglomeration of all these sources.
The curren t F reeBSD sc heduler has indep enden t runqueue structures on eac h
CPU and a simple balancing b et w een them. On eac h CPU, there are m ulti-
ple arra ys of task queues. Eac h arra y realizes a differen t sc heduling category
with decreasing priorities: realtime (for in terrupt threads, realtime tasks, k er-
nel threads, and in teractiv e user tasks), timeshare (for non-in teractiv e tasks),
and idle. Except for the timeshare category , the differen t queues are used to
7.4. COSCHEDULING IN FREEBSD 99
T able 7.1: F reeBSD task priorities, t yp es, and runqueues.
Priorit y T ask t yp e R unqueue mapping
0..47 In terrupt threads realtime[0..11]
48..79 Realtime user threads realtime[12..19]
80..119 T op half k ernel threads realtime[20..29]
120..151 In teractiv e user threads realtime[30..37]
152..171 Time sharing user threads
(ligh t CPU with negativ e nice)
timeshare[(x+0..16)%64]
172..203 Time sharing user threads timeshare[(x+17..45)%64]
204..223 Time sharing user threads
(hea vy CPU with p ositiv e nice)
timeshare[(x+46..63)%64]
224..255 Idle user threads idle[56..63]
realize differen t priorities, grouping four priorities p er queue and alw a ys execut-
ing tasks from the first non-empt y queue in a round robin (and in some cases
FIF O) manner. In earlier v ersions, this b eha vior w as also used for the timeshare
class, where the priorit y w as determined dynamically b y the nice lev el of a task
and its in teractivit y score. With ULE 2.0, this w as sligh tly mo dified. No w,
queues within the timeshare class are pro cessed round robin, and eac h task –
after its time slice ended or it ga v e of the CPU v olun tarily – is placed more
or less “ahead” of the curren tly pro cessed queue, dep ending on nice lev el and
recen t CPU usage. Dep ending on the in teractivit y score of a timeshare task, it
ma y also end up getting enqueued in one the realtime queues, so that it gets
pro cessed b efore an y non-in teractiv e tasks. The in teractivit y score is an in teger
from 0 to 100 and dep ends on the ratio of v olun tary sleep time to consumed
CPU time: tasks that v olun tarily sleep longer than they consume CPU receiv e
a score less than 50, tasks that compute more than they sleep receiv e a score
higher than 50. With the default settings, in teractivit y scores (after adjusting
them b y the tasks’ nice v alues) b elo w 30 are sc heduled in the realtime queues.
See T able 7.1 for a summary of the relation b et w een task priorit y , t yp e, and
runqueue.
The F reeBSD sc heduler do es kno w ab out the hierarc h y of a computer system
and uses this kno wledge in its balancing algorithms. The computer system is
represen ted as a tree, where eac h no de b elo w the ro ot no de corresp onds to a
shared cac he. This tree is used to lo cate CPUs, that are successiv ely further
a w a y , to either pull load from when a CPU is idle or to do a w ak e-up migration
on a more suitable CPU if the cac he affinit y for lo w er lev els has already expired.
In addition to this on-demand balancing, there is a global balancing p erio dically
running on the first CPU, whic h tra v erses the full tree to rep eatedly mo v e load
from the CPU along the highest loaded path to the CPU along the least loaded
path.
100 CHAPTER 7. INTEGRA TING T A CO
7.4.2 Design Outline
Compared to Lin ux, F reeBSD do es not pro vide a lot of infrastructure within
its sc heduler. That mak es some asp ects of the cosc heduling in tegration easier
and others more complex. On the one hand, there is not as m uc h functionalit y
to retain; on the other hand, there is also not as m uc h functionalit y to ease
the in tegration. F or example, F reeBSD do es not ha v e a v ersatile mec hanism
to manage groups of tasks (Step 1 of the con v ersion metho d), whic h can b e
used as a basis for cosc heduled sets. There is – of course – the concept of
a pro cess, whic h is a grouping mec hanism for tasks. But this is to o limiting
for most cosc heduling use cases. The closest equiv alen t are F reeBSD’s cpusets,
where arbitrary pro cesses can b e limited to certain CPUs. Eac h task already
con tains a reference to a cpuset and the infrastructure for managing cpusets
exists. It curren tly has the limitation, that all threads in a pro cess m ust b elong
to the same cpuset. Ho w ev er, according to the do cumen tation in the co de,
there is no tec hnical reason for this restriction; it is only to k eep the in terface
straigh t-forw ard, whic h w ould ha v e to b e extended an yw a y , so that the additional
configuration knobs for cosc heduled sets b ecome a v ailable. F or Step 2 (runqueue
generalization) no infrastructure exists that could b e re-used; this has to b e done
from scratc h.
The actual conceptual c hallenge in F reeBSD is Step 3 of the con v ersion
metho d: ho w can F reeBSD’s runqueue structure and supp orting algorithms b e
mo dified, so that a hierarc hical runqueue setup is supp orted? Using a similar
argumen tation as for Lin ux, w e w ould lik e to ha v e cosc heduling only for the
timeshare class, so that an ything more imp ortan t is able to w ork outside the
scop e of the cosc heduler (lik e F reeBSD’s in terrupt and k ernel threads). That is,
individual tasks of a cosc heduled set ma y get in terrupted for a short time, but
without preempting the whole set. This w ould mean that a CPU lo oks in to its
(lo cal) realtime queues for w ork and then, if it do es not find anything, it lo oks
in to the (hierarc hical) timeshare queues to find a coscheduled set to execute.
Ho w ev er, a complication arises, b ecause timeshare tasks ma y get enqueued in
the realtime queues instead of the timeshare queues, when they are considered
in teractiv e. If hierarc hical queues are only set up for the timeshare queues, but
w e still apply the in teractivit y logic to individual tasks, it w ould lead to the in-
teresting situation, where a task – when it is found to b e in teractiv e – will break
out of its cosc heduled set and get executed on its o wn in the non-hierarc hical
realtime queues. This w ould b e unacceptable b eha vior for man y cosc heduling
use cases; but w e do not w an t to lose this feature of the F reeBSD sc heduler ei-
ther, just b ecause of the cosc heduler in tegration – it w ould violate the rationale
of non-in trusiv eness.
T o prop erly in tegrate the in teractiv eness logic with cosc heduling, in teractiv e
tasks m ust not lea v e the cosc heduled set – their handling m ust b ecome hier-
arc hical as w ell. That said, in order to not upset F reeBSD’s k ernel tasks, the
“normal” realtime queues ha v e to b e retained. That is, a hierarc hical v arian t of
7.4. COSCHEDULING IN FREEBSD 101
the realtime queues is main tained in addition to the original realtime queues and
(hierarc hical) timeshare queues. The new set of hierarc hical queues can not only
hold in teractiv e tasks within a cosc heduled sets, but it also allo ws classifying
whole sets as in teractiv e. Ha ving b oth v arian ts at the same time also op ens the
opp ortunit y to supp ort cosc heduling of user-realtime tasks if desired (or ma yb e
ev en mixtures of realtime and timeshare tasks). Also, it can b e made a p er-set
run time configuration, whether the aforemen tioned breaking out of cosc heduled
sets is allo w ed for in teractiv e tasks (after considering the ramifications of this
decision on other cosc heduled sets). The in teractivit y of a cosc heduled set can
b e calculated from the sleep- and run time of the SE of the cosc heduled set itself.
The definition is straigh t-forw ard (b eing the same as for a task) and empha-
sizes the handling of a set as an en tit y: a set w ould only b e in teractiv e when
its tasks sleep and w ak e sim ultaneously . Individually in teractiv e but otherwise
unsync hronized tasks w ould lead to a cosc heduled set that is seldom sleeping
and w ould b e classified as non-in teractiv e, whic h ma y not b e what is desired
when m ultiple (indep enden t) tasks are represen ted b y an aggregating SE. Here,
it ma y b e more appropriate for an aggregated SE to inherit the the priorit y of
its most in teractiv e c hild, if an y . This will sc hedule in teractiv e tasks in a similar
manner to an unmo dified F reeBSD, no matter ho w tasks are aggregated and
whether they comp ete with cosc heduled sets. Just lik e an unmo dified F reeBSD,
this sc heme is susceptible to timing attac ks, where the system is flo o ded with
in teractiv e tasks (see, e. g., [88]).
F reeBSD’s w a y of scheduling has no direct notion of fairness. While this is
not a problem, when tasks ha v e to comp ete with other tasks for CPU time (or
a cosc heduled set with another cosc heduled set) as we desire exactly F reeBSD’s
t ypical b eha vior in that case, it mak es the CPU time distribution difficult, when
a task is rank ed against a set. Consider, for example, three tasks that b eha v e
iden tical from a sc heduler p ersp ectiv e, i. e., same priorit y , same in teractivit y (or
non-in teractivit y). If they w ere placed in the same runqueue, they w ould receiv e
the same amoun t of CPU time. The added hierarc hical asp ect should not c hange
that, ev en if t w o of these tasks ha v e b een aggregated and are no w represen ted b y
one SE whic h is in the same runqueue as the SE of the third task: the aggregated
SE should receiv e t wice the amount of CPU time. Similar observ ations hold for
non-iden tical tasks; arbitrary grouping should not c hange the asymptotically
receiv ed CPU time compared to an unmo dified F reeBSD. Giv en that timeshare
tasks are sc heduled differen tly , whether they are classified as in teractiv e or not,
w e can lo ok at one case at a time.
Conceptually , this problem do es not exist for in teractiv e tasks, b ecause they
cannot sta y in teractiv e indefinitely without going to sleep regularly , k eeping their
sleep-/run time ratio this w a y . In the example ab o v e, the aggregated SE with
t w o of the in teractiv e tasks w ould simply sta y in teractiv e un til b oth tasks ha v e
gone to sleep or lost their in teractivit y . Ho w ev er, if the runqueue for a sp ecific
in teractivit y score is saturated with alw a ys re-a w aking tasks, aggregated SEs
will not receiv e an appropriate amoun t of CPU time. If this is considered an
102 CHAPTER 7. INTEGRA TING T A CO
issue, a mec hanism to adjust the receiv ed CPU time is needed – for example
time slice length manipulation or temp orary priorit y adjustmen ts.
Within the timeshare queues, task priorities end up manipulating the se-
lection frequency of tasks; time slice length – while dep ended on the num b er
of tasks in a runqueue – is not influenced b y the priorit y . With the curren t
F reeBSD, the most imp ortan t task in a runqueue can b e pic k ed ab out 64 times
more often than the least imp ortan t task. Th us, w e can assign eac h priorit y a
w eigh t, so that the most imp ortan t timeshare queue priorit y has 64 times the
w eigh t of the least imp ortan t one. Then, we can aggregate w eigh ts (as describ ed
in Chapter 6) and con v ert the resulting weigh t bac k to a priorit y for the aggre-
gated SE. Unfortunately , the aggregated w eigh t will run out of the supp orted
range v ery quic kly , requiring a scaling mechanism. F reeBSD already do es some
scaling b y mapping the 72 timeshare priorities (152 to 223) on to 64 queues, when
calculating ho w man y runqueues ahead from the current p osition the task needs
to b e enqueued: index = 64
72 · ( pr ior ity − 152) . This is adapted for larger ranges
as follo ws.
Definition 27 (Priorit y to w eigh t con v ersion) . F or non-in teractiv e timeshare
tasks, the priorit y is con v erted to a w eigh t with a simple subtraction:
w eig ht = 224 − pr ior ity
Definition 28 (W eigh t to runqueue index) . A (p ossibly aggregated) w eigh t of
a SE is con v erted to a runqueue index b y scaling it according to a dynamically
determined reference w eigh t:
index = 64 · (︃ 1 − w eig ht
w eig ht r ef )︃
The reference w eigh t is dynamically adapted dep ending on the w eigh t v alues
represen ted b y the timeshare runqueues, ensuring that SEs are w ell spread out
across the runqueues and still receiv e their prop er quota of CPU time. F or
bac kw ards compatibilit y with F reeBSD, it is at least 72. That is, if there is no
aggregation, the runqueue index selection is iden tical to an unmo dified F reeBSD.
When a hea vier SE is encoun tered during enqueuing, the reference w eigh t is
increased to this v alue. This scales up the supp orted range and places ligh ter SEs
correctly from that p oin t forw ard again. Because ligh ter SEs are placed further
ahead, the reference w eigh t cannot simply b e the maximum of all enqueued SE
w eigh ts. Doing so w ould p enalize recen tly enqueued SEs, as SEs of the same
w eigh t will lik ely b e placed in fron t of them after a hea vy SE has b een dequeued.
Instead, the reference w eigh t deca ys linearly with eac h pro cessed runqueue, so
that it w ould reac h zero when all 64 runqueues ha v e b een pro cessed once. On
the one hand, this ensures that SEs, that do not increase the reference w eigh t,
are placed correctly with resp ect to already enqueued SEs. On the other hand,
7.4. COSCHEDULING IN FREEBSD 103
the reference w eigh t will b e alw a ys as least as hea vy as an y already enqueued
SE without, for example, ha ving to trac k the maxim um explicitly .
Definition 29 (Timeshare reference w eigh t) . The reference w eigh t w eig ht r ef is
adapted for ev ery enqueue op eration of a SE with w eight w eig ht . Let t b e the
n um b er of runqueues that w ere pro cessed since the last actual increase of the
reference w eigh t to a v alue w eig ht or ig . Then, the reference w eigh t is calculated
as follo ws:
w eig ht r ef = max {︃(︃ 1 − t
64 )︃ · w eig ht or ig , w eig ht, 72 }︃
Lo c king in F reeBSD is fine-grained with one lo c k p er runqueue. Only during
balancing actions t w o runqueues get lo c k ed simultaneously . This is v ery similar
to ho w Lin ux op erates. Hence, a similar setup with a lo c k p er SD should w ork
as w ell. Idle and wak eup balancing already resp ect the system top ology . Merely
the statistics gathering and actual task migration ha v e to b e made a w are of the
hierarc hical runqueues in Step 4. The p erio dic balancer, on the other hand,
alw a ys balances the whole system. This b ecomes prohibitive with cosc heduled
sets, where eac h set w ould ha v e to b e balanced individually to balance the whole
system. T o k eep the spirit of F reeBSD’s balancing, this can b e adapted as part of
Step 5 so that alw a ys whole coscheduled sets are balanced (without drilling do wn
in to an y c hild sets). F reeBSD’s logic of running the global balancer on ev ery n th
clo c k tic k on the first CPU, then translates to running the set balancer when
the n th tic k is c harged to a set b y its curren t master.
7.4.3 Comparison with Lin ux In tegration
The presen ted F reeBSD in tegration should reac h a similar lev el of functionalit y
as the Lin ux in tegration presen ted earlier. Though, some questions regarding
fine-tuning remain op en at this p oin t. F or example, F reeBSD accoun ts CPU
time in tic ks. The collectiv e con text switc h has to b e in tegrated so that it do es
not cause a systematic sk ew in the accoun ting, whic h could happ en if tasks are
consisten tly preempted just b efore or after a tic k elapsed.
Ov erall, it is difficult to sa y , whether the F reeBSD or the Lin ux in tegration
is more complex. The in tegration fo cus is certainly differen t: The most difficult
asp ect of the Lin ux in tegration, adapting the distributed load balancer (step 4),
is almost a non-issue in F reeBSD. Instead, F reeBSD requires more co de to b e
written from scratc h (task group mec hanism, step 1) and the more difficult
asp ects are fo cused around the runqueue reorganization (step 3). In the end,
mass migh t turn out to b e the deciding factor: with roughly just 3,000 lines of
co de, the F reeBSD sc heduler do es not ev en reac h a fifth of the size of the Lin ux
sc heduler – m uc h less co de to consider and to k eep functional o v er the course of
the in tegration.
104 CHAPTER 7. INTEGRA TING T A CO
7.5 Chapter Notes
The basic idea of Section 7.2 and a m uc h more condensed description of a less
dev elop ed v ersion of the Lin ux implemen tation in Section 7.3 w ere previously
published b y m yself in [84].
I decided to use F reeBSD as a second example, b ecause of its decidedly
differen t sc heduler structure compared to Lin ux. While m y initial decision for
F reeBSD w as based on a – as I later learned – outdated description of the
sc heduler, it still turned out to b e a go o d c hoice. In particular, it help ed to
shap e the minimal requiremen ts for applying T A CO, and it demonstrated once
more that un b ounded in tegers (the w eigh t aggregation) can p ose a problem,
esp ecially when y our storage destination can only represen t 64 distinct v alues.
Chapter 8
Analysis and Ev aluation
In this c hapter, the prop osed coscheduler design is ev aluated to see, whether
it is able to k ept true to the design rationales. The fo cus is hereb y mostly on
o v erhead that is in tro duced, as cosc heduling – just b y itself – has no inheren t
b enefits. Benefits only come in to pla y when exploiting one of the use cases, where
adv an tages are gained due to cosc heduling. Then, the b enefits ha v e to exceed
the o v erhead to mak e the trade-off w orth while. This c hapter demonstrates this
trade-off only exemplary for t w o scenarios, where cosc heduling has b een applied
blind, i. e., without explicitly kno wing whether exploiting cosc heduling w ould
yield an y b enefits. More in tense ev aluations of cosc heduling b enefits in general
can b e found elsewhere in the literature (see Chapter 2 for an o v erview and more
sp ecific p oin ters) and also in the third part of this thesis for use cases that w ere
iden tified b y the author.
T o recap, there are four design rationales:
V ersatilit y: Supp orting only single use cases limits the p oten tial and
is not attractiv e for system designers.
Scalabilit y: This is essential for to da y’s and up coming systems.
In teractivit y: Man y w orkloads to da y hav e an in teractiv e comp onen t;
cosc heduling m ust not in terfere with that.
Non-in trusiv eness: Keeping sc heduler b eha vior and most of the sc heduler
co de in tact, is the k ey for acceptance b y system designers
and application designers alik e.
The most often expressed criticism concerns the lac k of scalabilit y . In re-
searc h, this lac k is pro claimed b y supp orters of implicit cosc heduling sc hemes.
In industry , this is indirectly visible in VMw are’s size limit on cosc heduled sets.
Finally , the last attempt to add something similar to cosc heduling w as shot
do wn with that argumen t (cf. Section 1.2). Ho w ev er, the complete lac k of co-
sc heduling supp ort – ev en in a small scale – in curren t general purp ose op erating
systems silen tly p oin ts outs insufficiencies in the other areas.
105
106 CHAPTER 8. ANAL YSIS AND EV ALUA TION
The design presen ted in this thesis is sp ecifically crafted around v ersatilit y
and non-in trusiv eness (cf. c hapters 3, 6 and 7). While the other t w o rationales
w ere also k ept in mind, it is no w time to tak e a closer lo ok. In Section 8.1 the
selected st yle of doing the collectiv e context switc h is analyzed. Afterw ards,
the issue of fragmen tation in the hierarc hical setup is considered in Section 8.2.
Preempting larger cosc heduled sets imp oses additional costs, whic h are analyzed
in Section 8.3. Finally , Section 8.4 demonstrates the Lin ux instan tiation of the
design in three scenarios. The first demonstrated use case is the sc heduling of
parallel VMs, where cosc heduling a voids the very real problem of lo c k holder
preemption. The second use case are parallel programs, where cosc heduling
p oten tially helps b y enabling efficien t fine-grained sync hronization and an ex-
clusiv e cac he usage. Lastly , legacy capabilities are briefly c hec k ed b y not using
the existing cosc heduling functionalit y .
8.1 Collectiv e Con text Switc h
Cosc heduling in itself – esp ecially the strict v arian t promoted b y this thesis –
mak es the con text switc h more costly , b ecause of the necessary sync hronization.
And compared to pure space partitioning, nearly every con text switc h can b e
considered o v erhead. This o v erhead ultimately determines the length of the used
time slices: with con text switc h costs more or less constan t, a longer time slice
reduces the o v erhead relativ ely . Ho w ever, longer time slices also put in terac-
tiv e usage at a disadv an tage, so the length has to b e selected carefully . While
con text switc h costs ha v e b een examined in the past, m ulticore and esp ecially
cosc heduling asp ects ha v e b een ignored.
There are t w o t yp es of costs asso ciated with a con text switc h: direct costs
caused b y the execution of op erating system co de, and indirect costs caused
b y additional cac he misses due to p erturb ed cac he con ten ts. F ollo wing Liu and
Solihin [5], there are three categories of cac he misses: natural cache misses,
whic h w ould ha v e o ccurred an yw a y (and th us, do not accoun t to w ards context
switc h costs); cac he misses, b ecause some data w as replaced while other tasks
w ere running; and cac he misses, b ecause the original LR U sequence is reordered
due to accesses of other tasks. Esp ecially the latter b ecomes more problematic,
when the task or tasks are sp ecifically optimized to w ards the a v ailable cac he
size. While this cac he miss analysis w as done for unipro cessor systems, it can
b e transferred to shared cac hes as w ell – within reason.
8.1.1 Direct Costs
The direct con text switc h costs in the suggested cosc heduling sc heme are domi-
nated b y IPI latencies. Due to the tree-like notification sc heme, it scales with the
n um b er of hierarc h y lev els, i. e., logarithmic in the n um b er of CPUs. A con text
switc h across a fictiv e o cta-core system with a full binary tree as SD hierarc h y
8.1. COLLECTIVE CONTEXT SWITCH 107
C P U 1
C P U 2
C P U 3
C P U 4
C P U 5
C P U 6
C P U 7
C P U 8
A 1
A 2
A 3
A 4
A 5
A 6
A 7
A 8
E
E
E
E
E
E
E
E
B 1
B 2
B 3
B 4
B 5
B 6
B 7
B 8
(a) Simple IPI dissemination along the SD hierarc h y – a binary tree in this example.
Minimal in terruption p er CPU, but sync hronousness suffers and the sc heme do es
not handle IPI pro cessing dela ys w ell.
C P U 1
C P U 2
C P U 3
C P U 4
C P U 5
C P U 6
C P U 7
C P U 8
A 1
A 2
A 3
A 4
A 5
A 6
A 7
A 8
E
E
E
E
E
E
E
E
B 1
B 2
B 3
B 4
B 5
B 6
B 7
B 8
(b) IPI dissemination to all affected CPUs at once. Interrupted CPUs w ait for the
sc heduling information to tric kle along the SD hierarc hy .
Figure 8.1: Direct con text switc h costs b ecause of IPI latency .
is sho wn in Fig. 8.1a. Syn thetic b enc hmarks on a quad-core Core i7 860 (SMT
and turb o b o ost disabled) sho w that a collectiv e con text switc h is usually fin-
ished within 2 µ s, whic h corresp onds to the IPI latency . So ev en without further
optimizations, a fiv e lev el hierarc h y (at least 32 CPUs) w ould allo w time slices
of 1 ms while k eeping the direct context switc h costs b elo w 1%. Suc h small time
slices are inadvisable due to the indirect costs ho w ev er. With larger time slices,
sa y 10 ms, this scales to man ycore systems with only negligible o v erhead. Nev er-
theless, the direct costs could b e reduced with some optimizations. F or instance,
unnecessary hierarc h y lev els could b e (temp orarily) remo v ed; or the notification
mec hanism could proactiv ely send out IPIs to affected CPUs, whose first action
is then to w ait activ ely for sc heduling information to arriv e. The latter w ould
tak e the IPI latency mostly out of the cost equation; it also handles v ariances
in IPI deliv ery more gracefully . This is depicted in Fig. 8.1b.
F urther optimizations are conceiv able, lik e p erforming the sc heduling deci-
sion on b ehalf of dela y ed CPUs on already a v ailable CPUs, but these w ould ha v e
to b e carefully measured for their effectiv eness.
108 CHAPTER 8. ANAL YSIS AND EV ALUA TION
8.1.2 Indirect Costs
The indirect costs caused b y cac he misses are harder to grasp. On the one hand,
cac he misses dep end on the executed tasks; on the other hand, shared cac hes
complicate that ev en further. If multiple tasks use a shared cac he of a m ulticore
pro cessor comp etitiv ely at the same time, they inevitably cause evictions of data
of other tasks. Effectiv ely , dep ending on individual memory access patterns and
frequencies, ev ery task sees a smaller cac he. T ec hnically , this is another source of
natural cac he misses, whic h affects all sc heduling v arian ts to some degree: time-
sharing with and without cosc heduling and ev en pure space sharing. Though,
their o ccurrence v aries with the sc heduling sc heme.
Consider cosc heduling of indep enden t tasks, for example in the con text of
resource con ten tion a v oidance: If a sc heme is used, that creates man y indep en-
den t cosc heduled sets, the indirect costs migh t actually increase compared to an
unco ordinated sc heduling: with ev ery collectiv e con text switc h a set of tasks is
brough t to execution, whic h probably will not find its data in the cac he. Th us,
all CPUs run in to their bulk of cac he misses at the same time and then they
comp ete for memory bandwidth. With individual con text switc hes on each CPU
– either b ecause the sc heduling is unco ordinated, or the class based cosc heduling
v arian t is used – these cac he refills are more spread out in time. With parallel
applications with m ultiple tasks w orking on the same set of data, cosc heduling
generally impro v es the situation b y reducing the n um b er of applications that
sim ultaneously access a shared cac he. In the extreme case, cosc heduling is used
to assign cac hes exclusiv ely to a parallel application during its time slice. This
reduces natural cac he misses due to space sharing to zero.
With some p essimistic assumptions, it is p ossible to create some (almost)
w orst case v alues for the indirect con text switc h costs based on cac he size,
cac he/memory bandwidth and latency . In the w orst case, all CPUs sharing
a cac he switc h sim ultaneously and the whole cac he has to b e refilled. Either b e-
cause time slices are relativ e large, or there w ere simply man y other cosc heduled
sets sc heduled in b et w een so that no data is there to b e reused. This cac he refill
can b e done either with indep enden t memory accesses, where the full memory
bandwidth is the limiting comp onen t, or with dep enden t accesses (e. g., p oin ter
jumping), where the ac hiev able bandwidth is determined b y the memory latency .
This smaller bandwidth, ho w ev er, can usually b e realized p er CPU, so that the
actually realized bandwidth is again larger. T ogether with the cac he size, this
allo ws to calculate the time it w ould tak e to refill the cac he. Ev ery cac he miss
after a full refill w ould also ha v e happ ened without a con text switc h.
These refill times w ere determined for t w o exemplary pro cessors. The first
is a hexa-core AMD Opteron 8435 (DDR2-533 RAM, 5 MiB L3 cac he due to
HT Assist). The ac hiev able memory bandwidth is around 5 MiB/ms; with de-
p enden t accesses on all six cores, eac h core realizes an effectiv e memory band-
width of 0.7 MiB/ms. Th us, replacing the whole L3 cac he tak es ab out 1 ms for
b oth access t yp es. The second CPU is a quad-core In tel Core i7-2600 (DDR3-
8.2. FRA GMENT A TION 109
1333 RAM, 8 MiB L3 cac he, SMT), whic h has a higher memory bandwidth of
ab out 15 MiB/ms, but is not m uc h faster in the dep enden t case with around
0.8 MiB/ms for eac h of the eigh t CPUs. In this case, the dep enden t accesses
form the slo w er case, but it also tak es around 1 ms to refill the cac he.
Note, that this refill delay is not realized en-blo c after b eing sc heduled. More
lik ely it is someho w spread out across the time slice. If the time slice is rather
short or the w orking set rather small it migh t not ev en b e realized fully . A d-
ditionally , not all of this dela y actually constitutes indirect costs: some of the
cac he misses migh t b elong in to the natural category , esp ecially if the applica-
tion is not sp ecifically optimized to w ards cac he usage; and a scenario without
con text switc hes w ould also need time to pro cess the data (e. g., fetc hing it from
L3 cac he instead of memory).
Within these uncertain ties, it is p ossible to k eep the indirect con text switc h
costs on a m ulticore system b elo w 1% with time slices around 50 ms to 100 ms
if the system has similar c haracteristics as those ab o v e. Again, these indirect
costs also apply to unco ordinated sc heduling, whic h is only sligh tly b etter due
to (probabilistically) b etter spread out memory accesses. Short exp erimen ts
with the b enc hmarks, whic h are used later in this thesis, sho w ed that most (but
not all) also w ork with smaller time slices (e. g., 10 ms) without an y measurable
effects.
8.2 F ragmen tation
With supp ort for the differen t sc heduling constrain ts, it is p ossible to realize all
iden tified use cases – ev en sim ultaneously . I t is just a matter of creating prop erly
configured cosc heduled sets and filling them with tasks. The cosc heduler will
handle ev erything thro wn at it and giv e eac h cosc heduled set the guaran tees
it requested. That said, simply creating coscheduled sets without considering
system state at all migh t b e a source of fragmen tation, b ecause of conceptually
incompatible cosc heduled sets.
T raditionally , fragmen tation in parallel job sc heduling refers to situations,
where computational resources are un used despite the a v ailabilit y of tasks, b e-
cause of shortcomings of the CPU allo cation sc heme. This is further categorized
in to in ternal and external fragmen tation. In ternal fragmen tation o ccurs, when
a partition is larger than required and the extra CPUs are una v ailable for other
allo cations. External fragmen tation refers to situations, where enough capacit y
is a v ailable to execute a certain job, but the sc heduler is incapable to realize
this. In b oth situations, CPUs sta y idle unin ten tionally .
8.2.1 In ternal F ragmen tation
In ternal fragmen tation b ecomes a problem, when requests are arbitrarily sized
and the allo cation sc heme cannot realize arbitrary partitions. When lo oking at
the cosc heduling use cases in Chapter 2, arbitrarily sized requests are uncommon.
110 CHAPTER 8. ANAL YSIS AND EV ALUA TION
Most use cases need to b e aligned along the hardw are top ology , resulting in v ery
few sp ecific sizes. Only in some application design use cases it is p ossible to
size applications indep enden tly from the hardw are. And ev en there, it is not
t ypical for an application to require exactly , sa y , 17 cosc heduled CPUs. A t these
sizes, moldable or malleable applications can b e exp ected, as most of to da y’s
and up coming parallel programs are dev elop ed against some underlying parallel
substrate. A fixed degree of parallelism is no w ada ys only exp ected in the lo w er
single-digit range, e. g., small pip elines, or help er threads. A more relev an t
problem are applications with o ccasionally blo c king tasks, essentially a v ariation
of ev olving applications. These do not alw a ys k eep their partitions fully busy ,
and th us cause in ternal fragmen tation – unless of course resource isolation is
requested to prev en t, e. g., cac he in terference.
In ternal fragmen tation is a v oided b y one of t w o tec hniques. The short term
solution is to let idle CPUs within a cosc heduled set pic k up other w ork if
allo w ed (cf. Section 6.4.1). The more efficien t long term solution is to resize the
cosc heduled set and mo v e it within the SD hierarc h y (cf. Section 6.2). The latter
is less c heap to set up, but is b etter with resp ect to external fragmen tation.
8.2.2 External F ragmen tation
Compared to other cosc hedulers on, e. g., clusters, external fragmen tation is a
considerable less pronounced problem, b ecause cosc heduled sets can b e easily
migrated within m ulticore systems. These migrations are also relativ ely c heap –
except for migrations across NUMA domains, where the asso ciated memory can
b e a problem. In the past, migrations w ere found to b e the metho d of c hoice
to handle external fragmen tation, if it w ere not for the asso ciated costs [72,
89]. Despite this, external fragmen tation can o ccur. Either b ecause the load
balancer has not y et consolidated small SDs, or b ecause the issued com bination
of cosc heduled sets is conceptually incompatible due to unmatc hed partition
shap es or sizes. An example for the latter are certain com binations of sets
with the resource-sharing constrain t and the resource non-sharing constrain t; or
indep enden tly selected degrees of parallelism of m ultiple parallel applications.
Both ma y lead some holes in the sc hedule which cannot b e solv ed b y b etter
pac king.
It is the task of the op erating system to a v oid suc h unfa v orable situations.
It has to trade off partition sizes and shap es against the reasons for them b e-
ing requested. P oten tial consequences of not matc hing a particular request w ere
already discussed in Section 3.6. If these consequences are kno wn and not consid-
ered to o dire, partitions can b e rearranged. When the definition of coscheduled
sets is done b y the op erating system itself, all information regarding these trade-
offs is a v ailable and fragmen tation is usually not an issue, for example in setups
that a v oid resource con ten tion. One w a y to handle this in the con text of parallel
application is presen ted later in Chapter 9. Also, with enough (v aried) load in
the system, fragmen tation b ecomes less of an issue, as there will alw a ys b e a
small set or a task a v ailable, whic h can b e executed.
8.3. PREEMPTION 111
8.3 Preemption
Besides realizing time sharing, preemption is the essen tial mean to w ards in terac-
tiv e b eha vior. Preemption allo ws to cut the time slice of the curren t task short
in fa v or of another task that has b ecome more imp ortan t for some reason. This
reason lies either within the preempting task and it could b e something lik e a
w ak eup, migration, or priorit y c hange, or the reason could lie within the curren t
task, whic h has b ecome less imp ortan t, e. g., b ecause of a priorit y c hange.
As suc h, the concept of preemption transfers nicely in to the SD hierarc h y:
instead of a task preempting another task, one SE now preempts another SE.
The problem here is, that a sc heduling en tit y usually represen t a conglomeration
of tasks and the tasks’ prop erties m ust b e propagated in some w a y within the
hierarc h y to b e able to correctly assess the need for a preemption on a higher
lev el. F or a preemption c hec k, these propagations need to b e carried at most up
to p oin t in the SD hierarc h y , where the paths to w ards the top of the c hanged task
and the curren tly running task con verge . If a curren tly running task encoun ters
a p ossibly preemption causing prop ert y c hange, all runqueues to w ards the top,
where the c hange needs to b e propagated to, ha v e to b e c hec k ed for a no w more
imp ortan t candidate.
With the prop osed prop ortional share sc heduling, the only prop ert y of a task
that needs to b e propagated up w ards is its w eigh t. And here, only the loss of
w eigh t of the curren tly executing SE migh t b e a reason for a preemption, as
it migh t suddenly b e b ey ond its alloted time. Ho w ev er, with the CPU time
trac king sc heme suggested in Section 6.5, this c hec k b ecomes mo ot. The other
case, an increase in w eigh t, cannot cause a preemption b y itself. Hence, it is
p ossible to p ostp one the w eigh t propagation to reduce data structure conten tion
on the higher hierarc h y lev els.
One particular issue is the cosc heduling of in teractiv e tasks with compute
tasks, whic h for instance happ ens when unrelated tasks are represen ted b y an
aggregating SE in the next higher lev el. If not carefully co ded, the interactiv e
tasks ma y exp erience a drop in resp onsiv eness, b ecause the compute tasks con-
sume the whole share of a group. This is the case in Lin ux (also without the
cosc heduling extension), b ecause a preemption is only allo w ed, when a group
has not used a substan tial amoun t of its share, which the compute tasks ha v e
already done. (Coun terpro ductiv e is here also the emplo y ed minim um length
of a time slice, whic h – when com bined with w eigh t discrepancies – is able to
prev en t group execution for noticeable amoun ts of time.) In order not to fall
victim to this, a group m ust alw a ys b e allow ed to consume its remaining share
– without a lo w er limit on time slice length, whic h w ould giv e compute tasks
the abilit y to use time b ey ond the remaining share. The CPU time trac king
extension solv es this nicely .
112 CHAPTER 8. ANAL YSIS AND EV ALUA TION
8.4 Exp erimen ts
T o demonstrate the applicabilit y of the prop osed design in real life, the Lin ux
implemen tation w as sub jected to differen t situations, to observ e its b eha vior
First, coscheduling of parallel virtual mac hines is examined in Section 8.4.1,
whic h is probably the scenario where the effect of cosc heduling is immediately
noticeable. Then, parallel programs are cosc heduled in Section 8.4.2. Finally
in Section 8.4.3, the b eha vior of the cosc heduling implemen tation is c hec k ed
out, when nothing is explicitly cosc heduled in order to assess the short-comings
men tioned in Section 7.3.4.
8.4.1 Cosc heduling of Virtual Mac hines
The first scenario considers parallel virtual mac hines. Sp ecifically , m ultiple VMs
do parallel compilations of Lin ux 3.0 k ernels. Disk I/O was mostly a v oided b y
using RAM disks within the guests and enough ph ysical memory . All tests w ere
conducted on an In tel Core i7 860 (quad-core, 2.8 GHz, SMT and turb o b o ost
disabled) with 8 GiB RAM. Three differen t v arian ts of sc heduling VMs w ere
ev aluated in three differen t setups.
The three setups v ary the n um b er of CPUs p er VM as w ell as the parallelism
of the compilation within the VMs. Setup 1 has 4 VMs with 1,536 MiB RAM
and 4 vCPUs eac h. The compilation is done at a degree of parallelism of 8
to ensure mostly full utilization. Setup 2 uses 8 VMs with 768 MiB RAM and
2 vCPUs eac h and a parallelism of four. Finally , setup 3 targets sp ecifically
partial load: 8 VMs with 768 MiB RAM and 4 vCPUs eac h are used, but the
compilation is limited to at most t w o pro cesses at once. In all cases, the task
placemen t and load balancing within the VMs w as left to the guest OS, i. e.,
there w ere no attempts to optimize something within the guest.
The ev aluated sc heduling approac hes w ere Linux 3.2/KVM 0.14.1 without
cosc heduling, the same with cosc heduling, and VMw are ESXi 5.0.0-469512 as a
more sp ecialized solution. On the one hand, VMw are ESXi is the only h yp ervisor
with cosc heduling capabilities. On the other hand, it is limited to this single use
case due to b eing a h yp ervisor. A dditionally , a baseline w as measured for ev ery
approac h and setup b y executing a single VM with the approach in question.
Based on theoretic bin pac king of this baseline according to the setup, a relativ e
p erformance index can b e calculated, that sho ws the effectiv eness of a particular
sc heduling approac h.
The results are sho wn in T able 8.1. The deviation of individual exp eri-
men ts w as significan tly less than one p ercen t after w arm-up runs (except for
the v anilla KVM runs). Thus, the results sho w the a v erage of the run times of
the compilation within all VMs and t w o runs after this w arm-up phase. The
first observ ation is, that the only non-cosc heduling approac h – v anilla KVM –
is hop elessly b ehind in p erformance, whic h illustrates the extend of the problem
that lo c k holder preemption p oses for larger SMP VMs. Otherwise the results
v ary dep ending on whether the VMs are fully or partly loaded. F or fully loaded
8.4. EXPERIMENTS 113
T able 8.1: Comparison of differen t VM sc heduling approac hes.
Setup Approac h Absolute Baseline Relativ e
Setup 1
(4 quad-core VMs,
full load)
VMw are ESXi 354.7 s 90.6 s 102.1%
cosc hed. KVM 382.6 s 94.7 s 99.0%
v anilla KVM 9,697.5 s 93.4 s 3.9%
Setup 2
(8 dual-core VMs,
full load)
VMw are ESXi 716.8 s 176.3 s 98.4%
cosc hed. KVM 756.9 s 180.7 s 95.5%
v anilla KVM 1,208.8 s 180.6 s 59.8%
Setup 3
(8 quad-core VMs,
half load)
VMw are ESXi 868.2 s 173.4 s 79.9%
cosc hed. KVM 1,484.0 s 175.7 s 47.4%
v anilla KVM 1,723.7 s 175.9 s 40.8%
VMs, the cosc heduled KVM is only sligh tly b ehind the more sp ecialized ESXi
sc heduler, after accoun ting for ESXi b eing faster in the baseline exp erimen t.
This difference can b e attributed to the fact, that ev en the parallel compilation
runs within a guest ha v e some sequen tial phases. And ESXi is then able to exe-
cute m ultiple of these partially loaded VMs sim ultaneously – which also explains
the relativ e p erformance of more than 100% in setup 1. The Lin ux cosc heduling
implemen tation used for this exp erimen t did not y et supp ort load balancing of
small cosc heduled sets, whic h made an automatic adaptation of the cosc heduled
sets to the actually used CPUs imp ossible and precludes b etter results. This
limitation b ecomes significan t in setup 3, where a partial load within the VMs
is enforced, and the Lin ux implemen tation falls b ehind clearly . Though again,
this is a problem of the implemen tation – not of the design.
8.4.2 Cosc heduling of P arallel Programs
In this scenario, cosc heduling w as applied to sev eral parallel applications se-
lected from the Op enMP v ersion of the NAS P arallel Benc hmarks 3.3 [90]. The
follo wing b enc hmarks w ere selected:
EP .B: This b enc hmark is em barrassingly parallel. It neither includes signif-
ican t sync hronization nor do es its dataset exceed the p er-core cac hes.
Th us, this b enc hmark serv es as a reference case.
LU.W: This b enc hmark p erforms a LU decomp osition on a rather small dataset.
Con trary to all other NAS b enc hmarks, which use only the sync hro-
nization primitiv es of Op enMP for sync hronization, this b enc hmark
additionally mak es use of activ e w aiting.
CG.B: This b enc hmark is an implemen tation of the conjugate gradien t metho d.
It requires access to a larger dataset in short amoun ts of time, so that
some con ten tion within shared cac hes can b e exp ected, when executed
in parallel with other applications.
114 CHAPTER 8. ANAL YSIS AND EV ALUA TION
80
85
90
95
100
105
110
115
EP .B, 4 copies
Phenom 9950 Phenom II X4 940 Core 2 Quad 9400 Core i7 920
0
100
200
300
400
500
600
700
LU.W, 4 copies
Phenom 9950 Phenom II X4 940 Core 2 Quad 9400 Core i7 920
100
150
200
250
300
350
400
450
V anilla, 1 thread
V anilla, 2 threads
V anilla, 4 threads
Cosched, 4 threads
Cosched, 2 threads
V anilla, 1 thread
V anilla, 2 threads
V anilla, 4 threads
Cosched, 4 threads
Cosched, 2 threads
V anilla, 1 thread
V anilla, 2 threads
V anilla, 4 threads
Cosched, 4 threads
Cosched, 2 threads
V anilla, 1 thread
V anilla, 2 threads
V anilla, 4 threads
Cosched, 4 threads
Cosched, 2 threads
CG.B, 4 copies
Phenom 9950 Phenom II X4 940 Core 2 Quad 9400 Core i7 920
Figure 8.2: NAS parallel b enc hmarks, a v erage completion times in seconds with
standard deviation. F or eac h b enc hmark, eac h p oin t denotes the same amoun t
of o v erall w ork – they only v ary in the amount of threads and the emplo y ed
sc heduling strategy . While eac h exp erimen t w as carried out with activ e and
passiv e w aiting as another dimension, the diagram only sho ws the data p oin t
with the b etter p erformance, whic h is passiv e w aiting for the m ulti-threaded
v anilla cases and activ e w aiting for the others.
F or eac h b enc hmark, one to eigh t instances w ere executed in parallel using
sev eral differen t sc heduling configurations and mac hines. The sc heduling con-
figurations include single-threaded execution of the b enc hmark instances as w ell
as parallel execution with t w o and four threads p er instance with and without
ha ving them cosc heduled. A dditionally , the Op enMP implemen tation w as con-
figured to use either activ e or passiv e waiting. Eac h exp erimen t w as run three
times, measuring the time un til all instances ha v e finished. F or this ev aluation,
four differen t systems w ere used represen ting differen t hardware generations of
differen t v endors: an AMD Phenom 9950 (2.6 GHz), an AMD Phenom I I X4
940 (3.0 GHz), an In tel Core 2 Quad 9400 (2.66 GHz), and an In tel Core i7 920
(2.66 GHz, SMT and turb o b o ost disabled). All system run Lin ux 3.2, either
with or without in tegrated cosc heduling.
8.4. EXPERIMENTS 115
An in teresting subset of these results is sho wn in Figure 8.2. All p oin ts in
a diagram represen t the same amoun t of w ork, additionally eac h sho wn exp eri-
men t is able to fully utilize a system. Th us, if it w ere not for o v erheads of certain
configurations or arc hitectural effects, there should b e a horizon tal line p er dia-
gram p er system. The single-threaded execution has no parallel o v erhead. Th us,
there is no difference b et w een activ e and passiv e w aiting. The multi-threaded,
non-cosc heduled runs with four b enc hmark instances cause the system to b e
o v ercommitted. In the diagram, the results for passiv e w aiting are sho wn for
these cases, whic h outp erform those with activ e w aiting. With coscheduling and
the guaran tee of sync hronous execution of related threads, it is again p ossible
to use activ e w aiting, whic h a voids some o v erhead for sync hronization hea vy
w orkloads.
EP .B b eing a compute hea vy b enc hmark without substan tial sync hroniza-
tion is an example for a parallel application, whic h is mostly oblivious to the
emplo y ed sc heduling strategy: no matter whic h sc heduling configuration is cho-
sen, the p erformance is alw a ys the same. This also sho ws that the cosc hedul-
ing implemen tation do es not cause significan t o v erhead just b y itself. LU.W
uses activ e w aiting at application lev el. That is, ev en with Op enMP config-
ured for passiv e w aiting, p erformance drops significan tly as so on as the system
is o v ercommitted and threads do not run sync hronously an ymore. Except for
a non-parallel execution, only cosc heduling is able to sustain the p erformance
of suc h an application in an o v ercommitted system. CG.B represen ts an in-
teresting third class of applications. With neither to o m uc h synchronization
nor sync hronization mec hanisms outside of Op enMP , this b enchmark can b e
efficien tly executed with passiv e w aiting as the results on the Core i7 demon-
strate. How ev er, these results are sp ecific to the system arc hitecture, sp ecifically
the cac he arc hitecture. On the other systems, the emplo y ed sc heduling config-
uration mak es a difference due to their resp ectiv e effects on cache utilization.
With m ultiple instances utilizing the same cac he at the same time, p erformance
drops. Without coscheduling, the n um b er of sim ultaneous executed applications
remains uncertain. Though, the more threads each instance has, the higher is
the c hance of acciden tally cosc heduling related threads, whic h are then able to
share their data, reducing the cac he pressure. The b est p erformance for this
b enc hmark is ac hiev ed b y giving the last lev el cac he in its en tiret y to an in-
stance, i. e., to coschedule the instances across the whole pro cessor. This also
increases the predictabilit y as these uncertain ties are a v oided. As a sp ecial case,
the Core 2 quad has t w o last lev el cac hes: one for eac h pair of cores. Th us, it
is sufficien t to cosc hedule t w o thread instances to fully a void con ten tion within
those cac hes.
Ov erall, the cosc heduling implemen tation is able to realize its promised b ene-
fits. Though, it really dep ends on the application, whether the b est p erformance
can b e reac hed b y cosc heduling only , or if other sc heduling configurations reac h
similar p erformance lev els. A dmittedly , there probably are applications, where a
116 CHAPTER 8. ANAL YSIS AND EV ALUA TION
non-cosc heduling configuration yields b etter results, but results with cosc hedul-
ing will nev er b e w orse (except for o v erhead due to time-sharing) than executing
those applications in isolation.
8.4.3 Un used Cosc heduling F unctionalit y
When no application mak es activ ely use of coscheduling, the cosc heduler logically
degrades in to the sc heduler that it has b een b efore. The runqueue hierarc h y
is mostly un used and application tasks are enqueued in the original runqueues.
Still, there is some o v erhead asso ciated with the differen t op erations, a sc heduler
usually do es. Some CPUs ha v e to c hec k the upp er lev els of the hierarc h y to
see whether new load has arriv ed and to do regular house-k eeping on these
runqueues. Enqueuing and dequeuing of tasks has to go up in the hierarc h y
un til load on sibling runqueues is encoun tered. Due to a p ossible c hange in
priorit y , this migh t ev en ha v e to con tin ue b ey ond this p oin t to correctly assess
preemption.
Most of this w ork can b e a v oided in case the coscheduling infrastructure is
un used: upp er levels con tain at most one sc heduling en tit y , whic h is alw a ys b e-
ing executed. So, preemption is no issue and house k eeping and c hec king upp er
lev els can b e p ostp oned un til additional load actually arriv es. Esp ecially the
idea of p ostp oning house-k eeping already gains momen tum in curren t op erating
system sc hedulers in form of tic kless op eration, whic h sa v es energy b y allo wing
to k eep CPUs longer in deep er sleep states. And in the sp ecial case of virtual-
ized en vironmen ts, it additionally reduces load in the host. This lea v es actual
enqueuing and dequeuing of tasks as main sources of o v erhead in legacy op era-
tion. Due to the hierarc hical structure it could also b e a source of con ten tion,
when m ultiple CPUs con v erge on the same runqueues. Ho w ev er, the hierarc h y
is unlik ely to b e tra v ersed in a busy system as the b ottom la y er already con-
tains load b efore an enqueue, or it still has load after a dequeue. The necessary
rew eighing is not necessary and can b e done lazily . More critical is p ossibly a
system that is semi-busy – with runqueues regularly transitioning b et w een load
and no load. Here, it is helpful when enqueuing and dequeuing ha v e a fast-path
that is realized without getting a lo c k, when there is nothing to b e done b esides
rew eighing.
In addition to this o v erhead, there migh t b e sligh t b eha vioural c hanges in the
load balancing due to load b eing balanced only b et w een groups in the hierarc h y .
But this dep ends on ho w the original load balancer w ork ed in the first place.
Lo oking at p erformance, sev eral of the already describ ed exp erimen ts w ere
also run in non-o v ersubscrib ed scenarios, in whic h the cosc heduling functional-
it y – while still compiled in to the k ernel – remains un used. As the implemen ted
cosc heduler do es not y et realize an y of the describ ed lazy or lo c k-free optimiza-
tions, these results giv e an idea of the o v erhead in tro duced b y the runqueue hi-
erarc h y . F or Section 8.4.1, this is equiv alen t to the baseline exp erimen ts, where
only one VM w as executed at a time. Comparing the k ernel with cosc heduling
8.5. CHAPTER NOTES 117
to a v anilla k ernel, we see a p erformance drop of 1.4% (94.7 s vs. 93.4 s) only
for a fully loaded quad-core VM. The other t w o setups, whic h do not put the
system in to full load, do not sho w a noticeable difference (cf. T able 8.1). P art of
the exp erimen ts from Section 8.4.2 w ere runs with just one b enc hmark instance
getting executed utilizing four threads. Here, the k ernel v arian t with cosc hedul-
ing in tegrated w as at most 1% slow er than the one without cosc heduling. Going
further, the runqueue hierarc h y co de-paths w ere stressed with the messaging
b enc hmark of p erf (formerly kno wn as hac kb enc h) on the ev aluation system
from Section 8.4.1, sho wing a p erformance drop of 2.1% (11.19 s vs. 10.96 s for
10,000 lo ops).
8.5 Chapter Notes
The exp erimen ts from Section 8.4 ha v e b een previously published b y m yself
in [84] and [91], though the analysis is sligh tly extended.
This concludes the second part of this thesis, where design and creation of
the cosc heduler itself w as the main fo cus. In the up coming final part the fo cus
no w shifts to the application of cosc heduling. Individual c hapters presen t new
w a ys of utilizing coscheduling in m ulticore systems.
118 CHAPTER 8. ANAL YSIS AND EV ALUA TION
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