iswcs09 lecue presentation

Introduction Related Work Background Quality Model Optimization Experimentation Conclusion

Optimizing QoS-Aware Semantic WebService Composition

Freddy Lécué1

1The University of ManchesterBooth Street East, Manchester, UK

8th International Semantic Web Conference(ISWC 2009)

October 25th - 29th, 2009Westfields Conference Center, Washington, DC, USA

mailto:[email protected]


Outline

1 Introduction

2 Related Work

3 Background

4 Quality Model

5 Web Service Composition Optimization

6 Experimentation

7 Conclusion


Big Picture(Automation of) Web service composition in the Semantic Web.

Here we address ...Optimization for Web service composition wrt:

functional constraints (between services);and QoS constraints (of services).

s2

s1 s5

s3 s6

s7

T4

T2 T3 T6

T7

T8T1 T5

s4

s8


Most of approaches focus on optimization wrt:QoS or functional parameters.L. Zeng, B. Benatallah et al.Quality Driven Web Services Composition.In WWW, pages 411–421, 2003.F. Lecue, A. Leger and A. DelteilOptimizing Causal Link Based Web Service Composition.In ECAI, pages 45–49, 2008.

In our work:Optimization is achieved on both latter criteria!

SelectionQoS baseds1 s5

s3 s6

s7

T4

T2 T3 T6

T7

T8T1 T5

s4

s8

s12, s

22, s

32, ...




Semantics basedSelection s2

s1 s5

s3 s6

s7

T4

T2 T3 T6

T7

T8T1 T5

s4

s8




Selection

SelectionSemantics based

QoS baseds1 s5

s3 s6

s7

T4

T2 T3 T6

T7

T8T1 T5

s4

s8

s12, s

22, s

32, ...


Semantic Web Service in a Nutshell

Parameters (i.e., Input and Output) of Web services insemantic Web are concepts referred to in an ontology T :

WSDL-S, SA-WSDL (W3C Proposed Recommendation);OWL-S profile level;WSMO capability level.


Web Service Composition and its Semantic Links

Semantic Link: Semantic connection between services;... more particulary between Output and Input parameters;... denoted by sly,x and valued by SimT (Out_sy , In_sx);

SimT is reduced to the five matchmaking functions[M.Paolucci et al. ISWC’02, Li and Horrocks WWW’03]:

Exact i.e., T |= Out_sy ≡ In_sx ;PlugIn i.e., T |= Out_sy v In_sx ;Subsume i.e., T |= In_sx v Out_sy ;Intersection i.e., T 6|= Out_sy u In_sx v ⊥;Disjoint i.e., T |= Out_sy u In_sx v ⊥;

Web service: sxSemantic connection:

S Input Parameters

y

S Input Parameters

xS Output Parameters

y

S Output Parameters

x

Web service: sy

SimT

Out_sy In_sx


Web Service Composition and its Semantic Links

Semantic Link: Semantic connection between services;... more particulary between Output and Input parameters;... denoted by sly,x and valued by SimT (Out_sy , In_sx);

SimT is reduced to the five matchmaking functions[M.Paolucci et al. ISWC’02, Li and Horrocks WWW’03]:

Exact i.e., T |= Out_sy ≡ In_sx ;PlugIn i.e., T |= Out_sy v In_sx ;Subsume i.e., T |= In_sx v Out_sy ;Intersection i.e., T 6|= Out_sy u In_sx v ⊥;Disjoint i.e., T |= Out_sy u In_sx v ⊥;


S Input Parameters

y

S Input Parameters


y

S Output Parameters

x

Web service: sy

SimT

SlowNetWorkConnectionNetWorkConnection


Extra & Common Description in Semantic Links

Definition (Concept Difference)Let L be a DL, Out_si , In_sj be two concepts in L, and T be aset of axioms in L. A Concept Difference Problem (CDP),denoted as 〈L,In_sj\Out_si , T 〉 is finding a concept H ∈ L suchthat T |= H uOut_si ≡ In_sj uOut_si .

The Extra Description H represents what is underspecified inOut_si in order to completely satisfy In_sj ;⇒ Explain why Out_si and In_sj can not be chained by a

robust semantic link.

The Common Description CD lcs(Out_si , In_sj) refers toinformation required by In_sj and effectively provided byOut_si .


Extra & Common Description in Semantic Links

Definition (Concept Difference)Let L be a DL, Out_si , In_sj be two concepts in L, and T be aset of axioms in L. A Concept Difference Problem (CDP),denoted as 〈L,In_sj\Out_si , T 〉 is finding a concept H ∈ L suchthat T |= H uOut_si ≡ In_sj uOut_si .

e.g., in case of a semantic link valued by the Subsumematching type.

Web service: sx

S Input Parameters

x

S Output Parameters

x

S Output Parameters

Web service: sy

S Input Parameters

y

y


Composition Model

Process Model as a StatechartIts states refer to services;Its transitions are labelled with semantic links;with basic composition constructs such as Sequence,conditional branching (i.e., OR-Branching), concurrentthreads (i.e., AND-Branching).

Legend

Connection

Slow

Output Parameter

Input Parameter

T: Task

s: Service

Semantic Link sl

Network

Connection

Network

s1 s5

s2 s3

ANDBranching

s6

s7

sl15,7

sl12,3

sl11,4

sl15,6

sl14,5

sl16,8

T4

T2 T3 T6

T7

T8T1 T5

sl11,2 sl13,5

s4

s8

OR-Branching

Sequence

sl17,8


Quality Criteria for Semantic Links & Services

q(sli,j) for Elementary Semantic Links sli,jCommon Description rate qcd ∈ (0, 1]:

qcd(sli,j) =|lcs(Out_si , In_sj)|

|H∈〈L,In_sj\Out_si ,T 〉| + |lcs(Out_si , In_sj)|

Matching Quality qm ∈ (0, 1], valued by SimT (Out_si , In_sj )(Exact: 1, PlugIn: 3

4 , Subsume: 12 , Intersection: 1

4 ).

R. Kusters.Non-Standard Inferences in Description Logics.In Springer ISBN 3-540-42397-4, 2001.|.| refers to the size of ALE concept descriptions:

|>|, |⊥|, |A|, |¬A| and |∃r | are 1;|C u D| .

= |C|+ |D|; |∀r .C| and |∃r .C| is 1 + |C|.for instance |Speed u ∀mBytes.1M| = 3.








4 ).


S Input Parameters

y

S Input Parameters


y

S Output Parameters

x

Web service: sy

SimT

SlowNetWorkConnectionNetWorkConnection

(Subsume i.e., )12_








4 ).

q(si) for Elementary Services si

Execution Price qpr ∈ <+;Response Time qt ∈ <+.








4 ).

q(si) for Elementary Services si

Execution Price qpr ∈ <+;Response Time qt ∈ <+.

QoS-extended quality vector of a semantic link sli,j∗q (sli,j)

.= (q(si), q(sli,j), q(sj))


Quality Criteria for Composition

Quality Aggregation Rules for Compositions

Composition Quality Criterion

Construct Semantic Non FunctionalQcd Qm Qt Qpr

Sequential/ 1|sl|∑

sl qcd (sl)∏

sl qm(sl)∑

s qt (s) ∑s qpr (s)AND- Branching maxs qt (s)

OR-Branching∑

sl qcd (sl).psl∑

sl qm(sl).psl∑

s qt (s).ps∑

s qpr (s).ps

Legend

Connection

Slow

Output Parameter

Input Parameter

T: Task

s: Service

Semantic Link sl

Network

Connection

Network

s1 s5

s2 s3

ANDBranching

s6

s7

sl15,7

sl12,3

sl11,4

sl15,6

sl14,5

sl16,8

T4

T2 T3 T6

T7

T8T1 T5

sl11,2 sl13,5

s4

s8

OR-Branching

Sequence

sl17,8


Quality Criteria for Composition

Quality Aggregation Rules for Compositions

Composition Quality Criterion

Construct Semantic Non FunctionalQcd Qm Qt Qpr

Sequential/ 1|sl|∑

sl qcd (sl)∏

sl qm(sl)∑

s qt (s) ∑s qpr (s)AND- Branching maxs qt (s)

OR-Branching∑

sl qcd (sl).psl∑

sl qm(sl).psl∑

s qt (s).ps∑

s qpr (s).ps

A Quality Vector for Web Service Composition“A” way to differentiate compositions:

Q(c).= (Qcd(c), Qm(c), Qt(c), Qpr (c))


Composition Optimization Driven CSOP

CSOP Formalization (T , D, C, f )

T is the set of tasks (variables) {T1, T2, ..., Tn};D is the set of domains {D1, D2, ..., Dn} i.e., services;C is the set of constraints i.e., local CL and global CG;

e.g.,1|slA

i,j |∑slAi,j

qcd (slAi,j ) ≥ v , v ∈ [0, 1]

∑Ti

qpr (Ti ) ≤ v , v ∈ <+

f is an evaluation function.

Main Goal to AchieveAn assignment (si , Ti)1≤i≤n i.e., (service, task) Problem

with si,1≤i≤n ∈ Di,1≤i≤n;which satisfies all the constraints C;which is optimal in terms of QoS or functional quality.


A Genetic Algorithm based Method

Principles for computing the optimal solutionSimulating the evolution of an initial population untilsurvival of best fitted compositions satisfying constraints C.

GA ParametersGenotype.Initial Population: compositions randomly selected.Global, Local Constraints: CG, CL.Fitness Function: f (c)

ωcd Q̂cd (c) + ωmQ̂m(c)

ωpr Q̂pr (c) + ωtQ̂t (c)− ωpe.

genmaxgen

.∑

l∈ {pr,t,cd,m}

( ∆Q̂l

Q̂maxl (c)− Q̂min

l (c)

)2

Operators on Genotypes: crossover, mutation, selection.Stopping Criterion: until the constraints are met!


Benefits of Combining QoS and Functional CriteriaLimiting the costs of data integration.

Evolution of Composition Quality (up to |T | = 500, |s| = 500)

Complexity in the number of tasks and services;Variables: population size and number of generations;... but could be inappropriate.

GA Process vs. DL Reasoning (up to |T | = 30, |s| > 35)

DL reasoning is the most time consuming process!Large number of potential semantic links.Critical complexity of DL Difference.

Vs. State-of-the-art ApproachesBetter fitness values for the optimal composition;Faster convergence;


QoS-Aware Semantic Web Service Composition[Theoretical]

A general and extensible model to evaluate compositions:service (quality) level;semantic link (quality) level.

Optimization:CSOP formalization;A revisted GA based approach.

[Experimental (in Large Scale)]Good computation costs despite the off-line DL reasoning.

Future WorkExtension with more composition constructs;Considering a finer Difference operator;Dynamic Distribution of the CSOP on different peers.


Question?

iswcs09 lecue presentation

Documents

optimization wrt

web service composition

functional parameters

semantic linkssemantic

semantic connection

functional constraints

benatallah et

input parameters