Au oma ing he P ocu emen o Web Se ices
?
Oc a io Ma ín-Díaz, An onio Ruiz-Co és,
Amado Du án, Da id Bena ides, Miguel To o
Dp o. de Lenguajes y Sis emas In o má icos
E.T.S. de Ingenie ía In o má ica, Uni e sidad de Se illa
41012 Se illa, España - Spain
-
Phone: +34 95 455 3871 Fax: +34 95 455 7139
-
[email p o ec ed], [email p o ec ed],
[email p o ec ed], [email p o ec ed], [email p o ec ed]
Abs ac . As go e nmen agencies and business become mo e dependen on
web se ices, so wa e solu ions o au oma e hei p ocu emen gain impo ance.
Cu en app oaches o au oma ing he p ocu emen o web se ices su e om
an impo an d awback: nei he unce ain y measu es no non-linea , and com-
plex ela ions among pa ame e s can be used by p o ide s o speci y quali y-o -
se ice in o e s. In his pape , we look deeply in o he oo s o his d awback
and p esen a p oposal which o e comes i . The key poin o achie e his im-
p o emen has been using he cons ain p og amming as a o mal basis, since
i endows he model wi h a e y powe ul exp essi eness. A XML-based imple-
men a ion is p esen ed along wi h some expe imen al esul s and compa isons
wi h o he app oaches.
Keywo ds so wa e p ocu emen , web se ices, quali y-o -se ice, ade s.
1 In oduc ion
As go e nmen agencies and business become mo e dependen on web se ices, so -
wa e solu ions o au oma e hei p ocu emen gain impo ance. I is gene ally assumed
ha decision c i e ia o choosing so wa e packages s ems om he use equi emen s
hey should ul ill. The e a e di e en ypes o equi emen s such as manage ial, po-
li ical, and, o cou se, quali y equi emen s. The e a e a numbe o app oaches which
au oma e some ac i i ies o he p ocu emen , mos o hem ocus in quali y equi e-
men s. Howe e , hese app oaches su e om se e al d awbacks ha hampe hei use
when equi emen s ha p o ide s gua an ee include unce ain y measu es, non-linea
and complex ela ions among pa ame e s. In ac , i we wan o achie e a compe i i e
echnology based on web se ices, hei quali y-o -se ice is an impo an issue o be
aken in o accoun , becoming one o challenges o be sol ed in he nea u u e [31].
?
Suppo ed by he Spanish In e minis e ial Commission on Science and he Spanish Minis y o Science and Technology
unde g an s TIC2000-1106-C02-01, TIC2003-02737-C02-01 and FIT-150100-2001-78.
In his con ex , so wa e p ocu emen [4,5] becomes web se ices p ocu emen
(WSP), an ac i i y ocussed on he acquisi ion o web se ices equi ed by a web-
se ice-based sys em, hus i is a c i ical ac i i y o cu en web sys em de elope s.
Some ypical asks in ol ed in WSP a e:
–Speci ica ion o demands and o e s, which should be checked o consis ency in
o de o e i y hey do no con ain any inne con adic ion.
–Sea ch o o e s, which should be checked o con o mance in o de o e i y hey
ul ill he demand, so ha he selec ion is limi ed o such o e s.
–Selec ion o he bes choice acco ding o he assessmen c i e ia which is included
in he demand.
In his pape , we p esen a p oposal o au oma e he p ocu emen o web se ices.
Ou p oposal imp o es on o he s in ha i suppo s a symme ic speci ica ion model.
Thus, p o ide s can include in hei o e s equi emen s as complex as cus ome s in-
clude in hei demands. The key poin o achie e his imp o emen has been using
he cons ain p og amming as a o mal basis, since i endows he model wi h a e y
powe ul exp essi eness. A XML–based implemen a ion is p esen ed along wi h some
expe imen al esul s and compa isons wi h o he app oaches.
The es o he pape is s uc u ed as ollows. In Sec ion 2, we in oduce he no-
ions o asymme ic and symme ic speci ica ion models, as well as an o e iew o
ela ed wo ks. In Sec ion 3, we p opose he use o cons ain p og amming as a means
o achie ing a symme ic speci ica ion model. In Sec ion 4, we p esen b ie ly he main
implemen a ion aspec s o ou un- ime amewo k, oge he wi h some expe imen al
esul s. Finally, in Sec ion 5 we summa ise he p esen ed wo k and he immedia e u-
u e wo k.
2 Symme ic e sus Asymme ic Models
2.1 Asymme ic models
Le
S
be a mul idimensional space whose dimensions a e gi en by domains o quali y-
o -se ice pa ame e s. T adi ionally, a demand (

) has been iewed as a subspace in
S
, whe eas an o e (
!
) has been iewed as a poin in
S
. Thus, checking he con-
o mance amoun s o checking whe he he poin ( he o e ) belongs o he subspace
( he demand) o no . See Figu es 1.a and 1.b, espec i ely. This checking can be com-
pu ed easily by e alua ing
!
in

. As an example, i a web se ice owns he o e
!
=
MT T F
=120
g
, hen i is con o man o he demand

1
=
MT T F

100
g
be-
cause
120

100
, bu no o he demand

2
=
MT T F >
120
g
because
120
6
>
100
.
This in e p e a ion o con o mance esul s in a model which is asymme ic wi h
ega d o he exp essi eness o quali y-o -se ice speci ica ions. This seman ics makes
e y di icul o speci y o e s when i is needed some hing else han a poin , as an
example o speci y some unce ain yo a space. As mos o p og amminglanguages a e
able o check i a poin is inside a space, whe eas checking i a space includes ano he
space is a ha d ques ion, mos o pla o ms ha e adop ed an asymme ic speci ica ion
model. As well, hese app oaches wi h an asymme ic model usually own a limi ed
exp essi eness because condi ions a e es ic ed o simple exp essions in ol ing single
pa ame e s, so complex exp essions a e no allowed.
(a)
S S
(b)
Fig.1. Con o mance in asymme ic models.
2.2 Symme ic models
Al e na i ely, an o e can be also conside ed as a sub-space, jus as demands, so ha
i ep esen s he anges o quali y-o -se ice alues ha he co esponding web se ice
gua an ees o supply. In his way, an o e (
!
) is con o man o a demand (

) when-
e e he o e ’s sub-space is inside he demand’s sub-space (see Figu e 2.a), o he wise
he o e is no con o man (see Figu e 2.b). As an example, i a web se ice owns
he o e
!
=
MT T F >
=120
g
, hen i is con o man o he ollowing demand

1
=
MT T F >
=100
g
, bu no o he demand

2
=
MTTF >
120
g
because he
o e ’s ins ance alue {
MT T F
= 120
} is ou o he demand’s space.
This in e p e a iono con o mance esul s in asymme icmodel becausequali y-o -
se ice in demands and o e s can be speci ied in he same way. This seman ics makes
he o e gua an ee he comple e ange, no only a conc e e alue, i.e., we can no make
any assump ion on a conc e e alue, because i is equally possible any alue in he sub-
space, and he e is no con ol o ge a conc e e alue. As well, symme ic app oaches
usuallyachie eag ea e dealo exp essi eness ospeci yquali y-o -se ice,since he e
is usually no es ic ion on he numbe o in ol ed pa ame e s o ype o ope a o s, so
ha non-linea o mo e complex exp essions a e allowed.
(b)
(a)
SS
Fig.2. Con o mance in symme ic models.
2.3 Rela ed wo k
Figu e 3 shows a compa a i e s udy among he mos p ominen (as a as we know)
quali y-awa e app oaches o WSP. B ie ly:
–The UDDI Ex ension (UDDIe) [28] is based on he UDDI (Uni e sal Desc ip-
ion Disco e y and In eg a ion) se ices. UDDIe owns an asymme ic model when
speci ying demands and o e s.
IBM's
WSME
MME
HP's MME
Se ice
UDDIe
Ou
P oposal
Cus ome 's P o ide 's
Name-Value Pai P ope ies
S a ic/Dynamic Binding
Sc ip s o Rule-based Reqs.
Composi ion
Single-Pa ame e Cons ain s
on Pa ame e s o Se ice
(expandable)
Name-Value
Pai
P ope ies
Single-Pa .
Condi ions
on P ope ies
(Quali ie s)
Composi ion
Mul iple-Pa ame e
Cons ain s on
Pa ame e s o Se ice
Da a S uc u ing
Da a Dic iona y:
p e-de . basic ypes
sequences
eco ds
DAML+OIL On ology:
da a ypes and ypes
subsump ion
Blue Pages
Ca alogues:
p e-de . basic ypes
ca alogue ex ension
basic and de i ed p.
Dynamic View:
The P ocess Model
Ad e isemen /Submission
Que y/Submission
Ma chmaking
Selec ing P o ide s' O e s
Ad e ising
Que ying
B owsing
Publishing
Sea ch and Disco e y
C ea ing Ca alogues
O e s Submission
Demands Submission
Ma chmaking
Ca alogues,
Pa ame e s & Measu es
S akeholde s
P o ide s
Cos ume s
Ad e ise s
Reques o s
P o ide s
Consume s
P o ide s
Cos ume s
Quali y-o -Se ice
Documen s
Ad e isemen s
Que ies
Ag eemen s
Se ice O e s
& Reques s
Publishing
Inqui y
Demands
O e s
Ag eemen s
S a ic View: The Lexicon
The
Re e ence
Model
Fig.3. A compa ison o quali y-awa e app oaches o WSP.
–TheHP’sMa chmakingEngine(MME) [10] is basedon heDAML(DARPAAgen
Ma kup Language) seman ic web language [2]. I is he closes p oposal o ou s,
because i owns a symme ic model o speci y quali y-o -se ice, and i uses cons-
ain s o do i , so i owns a g ea exp essi eness. As well, i uses a Desc ip ion
Logic DL’s sol e as a mean o ca ying ou he WSP- ela ed asks. Ne e heless,
he e is no cu en ly any DL’s sol e e sion able o p ocess some o he mos
complex exp essions which can be speci ied in MME.
–The IBM’sWeb Se ices Ma chmaking Engine (WSME) [12], which is ela ed
o Web Se ice Le el Ag eemen (WSLA) [15,17], is based on he CORBA/ODP
ade se ice. I owns an asymme ic model and he e is no op ima ion o he se-
lec ion because sea ch esul s a e only he lis s o con o man o e s. None heless,
he e is a di e ence: ela ionships be ween demands and o e s a e bila e al. In he
same way quali y-o -se ice in o e s is based on pa ame e / alue pai s whe eas
demands impose condi ions on hem, i is also allowed ha demands de ine hei
own quali y-o -se ice pa ame e s whe eas he o e s impose condi ions on hem.
As an example, le an o e be gi en by he ollowing quali y-o -se ice speci i-
ca ion
!
=
me:M T T F
=120 &
you :na ional i y
2
BE;:::;UK
gg
and a
demand

=
me:na ional i y
=
IS
g
&
y ou :M T T F >
100
g
, hen he o e
!
is no con o man o he demand

, because he condi ion i imposes on he de-
mand ( he Eu ope Union membe ship) is no ul illed, despi e o he o e ul ills
condi ions imposed by he demand.
–O he languages o speci ying quali y-o -se ice and ade se ices he Quali y-
o -se iceModelingLanguage(QML)[8], heNoFunlanguage[6],and he CORBA
ade se ice [22]. These p oposals a e no di ec ly ela ed o WSP.
3 Suppo ing WSP wi h Cons ain P og amming
We ha e chosen ma hema ical cons ain s as he way o speci ying quali y-o -se ice
in demands and o e s. In his way, checking con o mance can be ca ied ou jus as
a cons ain sa is ac ion p oblem (CSP) o a cons ain sa is ac ion op imisa ion p o-
blem (CSOP) [7,11,18,29]. In gene al, CSP-based modelling is qui e simple and in ui-
i e (in mos cases) in he con ex o p oblems which we a e dealing wi h. Cons ain
p og amming is an excellen suppo o symme ic speci ica ions models, because i
makes possible o check whe he a space is included in ano he one, being hese spaces
ea ed as cons ain s. Ou p oposal owns a symme ic speci ica ion model wi h a g ea
deal o exp essi eness because o using cons ain s.
3.1 Cons ain p og amming in a nu shell
Cons ain P og amming (CP) has ecen ly a ac ed high a en ion among expe s om
many a eas because o i s po en ial o sol ing ha d eal-li e p oblems. No only i is
based on a s ong heo e ical ounda ion, bu i is an a ac ing widesp ead comme cial
in e es , as well. Cons ain s o malise hose dependencies in physical wo lds and hei
ma hema ical abs ac ions na u ally and anspa en ly. A cons ain is simply a logical
ela ion among se e al a iables, each aking a alue in a gi en domain. The cons ain
hus es ic s he possible alues ha a iables can ake, and i ep esen s a pa ial in-
o ma ion abou he a iables o in e es . An impo an ea u e o cons ain s is hei
decla a i e manne , i.e., hey speci y wha ela ionships mus hold wi hou speci ying
a compu a ional p ocedu e o en o ce hem. CP is he s udy o compu a ional sys ems
based on cons ain s. The idea o CP is o sol e p oblems by s a ing cons ain s ( e-
qui emen s) abou he p oblem a ea and, consequen ly, inding solu ion sa is ying all
he cons ain s.
The ea lies ideas leading o CP may be ound in he A i icial In elligence da ing
back o six ies and se en ies. The scene labelling p oblem [30] is p obably he i s
cons ain sa is ac ion p oblem ha was o malised. The main s ep owa ds CP was
achie ed when Gallai e [9] and Ja a & Lassez [14] no ed ha logic p og ammingwas
jus a pa icula kind o cons ain p og amming.The basic idea behind Logic P og am-
ming (LP), and decla a i e p og amming in gene al, is ha he use s a es wha has o
be sol ed ins ead o how o sol e i , which is e y close o he idea o cons ain s.
The e o e he combina ion o cons ain s and logic p og amming is e y na u al, and
Cons ain Logic P og amming (CLP) makes a nice decla a i e en i onmen o sol -
ing p oblems by means o cons ain s. Howe e , i does no mean ha CP is es ic ed
o CLP. Cons ain s we e in eg a ed o ypical impe a i e languages like C++ and Ja a,
as well.
The nowadays eal-li e applica ions o CP in he a ea o planning, scheduling and
op imisa ion ise he ques ion i he adi ional ield o Ope a ions Resea ch (OR) is a
compe i o o an associa e o CP. The e is a signi ican o e lapo CP and OR in he ield
o NP-Ha d combina o ial p oblems. While he OR has a long esea ch adi ion and
( e y success ul) me hod o sol ing p oblems using linea p og amming, he CP em-
phasis is on highe le el modelling and solu ions me hods ha a e easie o unde s and

by he inal cus ome . Mos ecen ad ances p omise ha bo h me hodologies can ex-
ploi each o he , in pa icula , he CP can se e as a oo pla o m o in eg a ing a ious
cons ain sol ing algo i hms including hose de eloped and checked o be success ul
in OR. As he abo e pa ag aphs show, he CP has an inne in e disciplina y na u e. I
combines and exploi s ideas om a numbe o ields including A i icial In elligence,
Combina o ial Algo i hms, Compu a ional Logic, Disc e e Ma hema ics, Neu al Ne -
wo ks, Ope a ions Resea ch, P og amming Languages, and Symbolic Compu a ion.
Cu en ly, we see wo b anches o CP, namely cons ain sa is ac ion and cons ain
sol ing. Bo h sha e he same e minology bu he o igins and sol ing echnologies a e
di e en . The o me deals wi h p oblems de ined o e ini e domains and, cu en ly,
p obably mo e han 95% o all indus ial cons ain applica ions use ini e domains.
The e o e,we deal wi h cons ain sa is ac ion p oblems mos ly in his pape . The la e
sha es he basis o CP, i.e., desc ibing he p oblem as a se o cons ain s and sol -
ing hese cons ain s. Bu now, he cons ain s a e de ined (mos ly) o e in ini e o
mo e complex domains. Ins ead o combina o ial me hods o cons ain sa is ac ion,
he cons ain sol ing algo i hms a e based on ma hema ical echniques such as au o-
ma ic di e en ia ion, Taylo se ies o New on me hod.
Cons ain Sa is ac ion P oblems [29] ha e been a subjec o esea ch in A i icial
In elligence o many yea s. A Cons ain Sa is ac ion P oblem (CSP) is de ined as a se
o a iables each anging on a ini e domain, and a se o cons ain s es ic ing all he
alues ha a iables can simul aneously ake. A solu ion o a CSP is an assignmen o
a alue om i s domain o e e y a iable, in such a way ha all cons ain s a e sa is ied
a once. We may wan o ind: i) jus one solu ion, wi h no p e e ence as o which
one, ii) all solu ions, iii) an op imal, o a leas a good solu ion, gi en some objec i e
unc ion de ined in e ms o some o all o a iables. Solu ions o a CSP can be ound
by sea ching (sys ema ically) h ough all possible alue assignmen s o a iables.
In many eal-li e applica ions, we do no wan o ind any solu ion bu a good solu-
ion. The quali y o solu ion is usually measu ed by an applica ion dependen unc ion
called objec i e unc ion. The goal is o ind such solu ion ha sa is ies all he cons-
ain s and minimise o maximise he objec i e unc ion, espec i ely. Such p oblems
a e e e ed o as Cons ain Sa is ac ion Op imisa ion P oblems (CSOP), which con-
sis s o a s anda d CSP and an op imisa ion unc ion ha maps e e y solu ion (comple e
labelling o a iables) o a nume ical alue [29].
3.2 Consis ency and con o mance
Whene e a new demand o o e is submi ed, i s consis ency needs o be checked,
i.e., whe he o no i con ains any inne con adic ion. This is in e p e ed as a CSP, so
ha i he co espondingCSP is sa is iable, hen he demand o o e can be conside ed
as consis en . The co esponding CSP o a demand o o e is composed o all he
cons ain s i con ains. On he o he hand, he bes choice selec ion ega ding wi h a de-
mand implies he p e ious checking o con o mance, because he sea ch is educed o
con o man o e s. As we a e using cons ain p og amming, checking o con o mance
lies in de e mining whe he each and e e y solu ion o he o e ’s CSP is also a solu ion
o he demand’s CSP.
In his way, he co esponding CSP o checking he con o mance is cons uc ed
acco ding o he de ini ion gi en in [18]:
con o mance
(
!; 
)
,
sa
(
c
!
^:
c

)=
alse
whe e
!
is he o e and
c
!
i s co esponding CSP,

is he demand and
c

i s co es-
ponding CSP, and
sa
is a unc ion ha we iden i y wi h he CSP sol e which is being
used. I can be applied on a CSP
c
so ha i e u ns one o he ollowing esul s:
ue
i
c
is sa is iable,
alse
i no , and
?
i he sol e canno de e minewhe he
c
is sa is iable
o no .
3.3 Op imali y
Mo e o en han no , i is possible o ha e se e al o e s which a e con o man o he
same demand o a web se ice, hen we should selec ha o e which is he bes
choice. This selec ion is ca ied ou acco ding o he assessmen c i e ia he cus ome
includes in his o he demand. These c i e ia may be gi en by u ili y unc ions [3,16,
21] which, in gene al, ha e he signa u e
U
:

!
[0
;
1]
whe e

is he measu ing
domain o a quali y-o -se icepa ame e . U ili y unc ions assign an u ili y assessmen
( anging om 0 o 1) o e e y quali y-o -se ice alue i can ake, so he g ea e he
assessmen , he be e he conside a ion o he cus ome . The e o e, u ili y unc ions
allow he es ablishmen o an objec i e c i e ia, gi en by cus ome s, in o de o selec
hose o e s which be e ul ill he demands. Figu e 4 shows se e al u ili y unc ions
co esponding o examples in his sec ion.
U ili y o Mean Time To Failu e
0,5
1
0,25
1209060
MTTF
0,75
45 75 105 140
010 20 30
0,5
1
0,25
0,75
05152535
MTTR
U ili y o Mean Time To Repai
Modem Modem
ISDN
ISDN Modem
ADSL ISDN
ADSL
Modem
ISDN
ADSL
ADSL
U ili y o Media Suppo
MEDIA
0,5
1
0,25
0,75
0
{ }
Fig.4. U ili y unc ions o
MTTF
,
MT T R
, and
MEDIA
.
Al houghwe can make useo anykind o unc ion o speci y u ili y unc ions,linea
piecewise unc ionsa e o en he p e e ed.As an example, he u ili y unc ion o a nu-
me ic quali y-o -se icepa ame e can be de inedby means o polylines de e mined by
a sequence o coo dina epoin s such as
(
x
1
;u
1
)
;
(
x
2
;u
2
)
;:::;
(
x
n
;u
n
)
, whe e e e y
x
ep esen s a alue in he measu ing domain o he quali y-o -se ice, and
u
i s assess-
men in he ange [0,1]. The co esponding u ili y unc ion is hen gi en by:
U
(
x
)=
8
>
>
<
>
>
:
u
1
+
u
2
;
u
1
x
2
;
x
1
(
x
;
x
1
)
i
x
1

x<x
2

u
n
;
1
+
u
n
;
u
n
;
1
x
n
;
x
n
;
1
(
x
;
x
n
;
1
)
i
x
n
;
1

x

x
n
?
i
x<x
1
o
x>x
n
We a e no usually in e es ed in compu ing he u ili y assessmen o an unique
quali y-o -se ice pa ame e , bu on maximising he global assessmen o o e s in
o de o selec he bes one, being hese o e s con o man o he demand. Ne e he-
less, we can no compu e he maximum o e s’u ili y assessmen s when compa ing
hem. As an example, le he ollowing o e s
!
1
=
60

MT T F

120
g
and
!
2
=
90

MT T F

110
g
. In ui i ely, he i s is be e , because i
MT T F
=120
hen
U
(
!
1
) = 1
. Howe e , he o e is gua an eeing he comple e ange, no only a
conc e e alue, so we can no make such assump ion because i is equally possible ha
MTTF
=60
, and he e is no con ol o ge a conc e e alue. The e o e, we compa e
he minimum u ili y assessmen s o o e s. In his way, he la e o e is he be e , be-
cause i
MTTF
=90
hen
U
(
!
2
) = 0
:
5
, whe eas he wo s assessmen o he i s
o e is 0.25, a mos . Fo mally, he bes o e (
!
S
) can be de ined as:
!
S
=
!
2


 8
!
i
2


;
!
g U

(
!
)
U

(
!
i
)
whe e
!
and
!
i
s and o o e s in he se


o con o man o e s o he demand

, and
he
U

(
!
)
u ili y unc ion o an o e
!
acco ding o assessmen c i e ia in demand

is
de ined as:
U

(
!
)=
min
P

2
c
!
w


U

(

)
s c
!
whe e

ep esen s a quali y-o -se ice pa ame e which is in ol ed in he o e ’s CSP
c
!
, and
U

(

)
i s u ili y unc ion, and
w


i s assigned weigh , acco ding o assessmen
c i e ia in demand

. On he o he hand, weigh s a e needed o exp ess ha a quali y-
o -se ice pa ame e is p e e ed o ano he .
3.4 An example o con ain -based quali y-o -se ice speci ica ion
Figu e 5 shows se e al ca alogues, demands, and o e s w i en in QRL [23,26], he
language which we ha e de ined o speci ying quali y equi emen s. Figu e 4 shows
he g aphical ep esen a ion o u ili y unc ions appea ing in Figu e 5. These demands
and o e s will be used in he examples along hese pa ag aphs.
In his case, he in ol ed quali y-o -se ice pa ame e s a e he Mean Time To
Failu e (MTTF), heMean Time To Repai (MTTR),and heMedia Sup-
po (MEDIA).No e he included demand and o e s a e all consis en , because hei
co esponding CSP a e sa is iable, as well as o e s a e con o man o he demand,
because he co esponding CSP o checking he con o mance a e no sa is iable, ac-
co ding o de ini ions in Sec ion 3.2.
Since bo h o e s a e con o man o he demand, we will ha e o compu e he u i-
li y unc ions o compa e hem. Acco ding o de ini ions in Sec ion 3.3, bo h o e s
own
U
(
MTTF
= 110) = 0
:
83
and
U
(
MT T R
=10) = 0
:
8
, elazquez owns
U
(
MEDIA
)=1
, and zipi owns
U
(
MEDIA
)=0
:
5
. The e o e, u ili y assessmen
o elazquez is
0
:
9

0
:
83 + 0
:
05

0
:
04 + 0
:
05

1=0
:
84
, and u ili y assessmen o
zipi is
0
:
9

0
:
83 + 0
:
05

0
:
04+0
:
05

0
:
5= 0
:
815
, so he bes o e is elazquez.
// A ca alogue o Reliabili y- ela ed QoS pa ame e s
ca alogue Reliabili y {
MTTF {
desc ip ion: "Mean Time o Failu e";
domain: eal [0,+in ) minu e;
};
MTTR {
desc ip ion: "Mean Time To Repai ";
domain: eal [0,+in ) minu e;
};
}
// A ca alogue o Mul imedia- ela ed QoS pa ame e s
ca alogue Mul imedia {
MEDIA {
desc ip ion: "Media Suppo ";
domain: se { modem, ISDN, ADSL };
}
}
// Web se ice o e supplied by Velazquez
using Reliabili y, Mul imedia;
o e o IVideoSe e {
O1: MTTF >= 110 and MTTF <= 120;
O2: MTTR > 5 and MTTR <= 10;
O3: MEDIA = {ADSL,ISDN,modem};
}
c) Se e al o e s.
a) Ca alogues o quali y-o -se ice pa ame e s.
// Web se ice demand o IVideoSe e
using Reliabili y, Mul imedia;
demands o IVideoSe e {
D1: MTTF / (MTTF + MTTR) >= 0.9;
D2: MEDIA includes {modem,ISDN};
}
assessmen {
MTTF {90, { (0,0), (90,0.5), (120,1) } };
MTTR {05, { (0,1), (20,0.6), (30,0) } };
MODEM {05,
case MEDIA = { } : 0.01;
case MEDIA = {modem} : 0.1;
case MEDIA = {ISDN} : 0.3;
case MEDIA = {ISDN,modem} : 0.5;
case MEDIA = {ADSL} : 0.9;
case MEDIA = {modem, ADSL} : 1;
case MEDIA = {ISDN, ADSL} : 1;
case MEDIA = {modem, ISDN, ADSL} : 1;
}
}
b) A demand.
// Web se ice o e supplied by Zipi
using Reliabili y, Mul imedia;
o e o IVideoSe e {
O1: MTTF >= 110 and MTTF <= 120;
O2: MTTR > 5 and MTTR <= 10;
O3: MEDIA = {ISDN,modem};
}
Fig.5. Demands and o e s w i en in QRL.
4 Implemen a ion and Expe imen al Resul s
4.1 O e iew o he p o o ype’s a chi ec u e
We a e de elopinga p o o ypeo a un- ime amewo k o WSP [19,20,24,27], whose
p elimina y e sion is a ailable a h p://www.lsi.us.es/˜oc a io. In his
pape , we gi e a b ie e iew, oge he wi h some expe imen al esul s we ha e ecen ly
ob ained. A componen s iew o he un- ime amewo k is shown in Figu e 6.
Selec ing a mul i-le el a chi ec u e along wi h he deploymen o he componen s
as web applica ions o web se ices ha e been c i ical design decisions. Componen s
a e spli up among he uppe use -in e ace le el, he in e media e se ice and u ili y
le els, and he bo om eposi o y le el. These componen s can be eusable and in e -
changeable.Se ice le el includes hose componen swhich implemen he IImpo -
Se ice in e ace ( unc ions ela ed o submission o demands and sea ching o bes
con o man o e ), and he IExpo Se ice in e ace ( unc ions ela ed o submis-
sion o o e s).
These componen s ha e need o in oking checkings o consis ency, con o mance,
and op imum sea ch. These unc ions a e implemen ed by he Quali y T ade Web Se -
ice [19] a he u ili y le el. Each unc ion has a simila ope a ion:
1. I akes he in ol ed demands and o e s w i en in XML as pa ame e s.