Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Towards Scalability of Quality DrivenSemantic Web Service Composition

Freddy Lécué1 Nikolay Mehandjiev1

firstname.lastname@manchester.ac.uk

1The University of ManchesterBooth Street East, Manchester, UK

IEEE 7th International Conference on Web Services(ICWS 2009)

July 6th - 10th, 2009Los Angeles, CA, USA

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Outline

1 Introduction

2 Related Work

3 Background

4 Quality Model

5 A Scalable Approach

6 Experimentation

7 Conclusion

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

Rather than Optimization (NP Hard!), we address ...Scalability of quality driven semantic service composition:

by selecting compositions based on:functional constraints;and QoS constraints.

s2

s1 s5

s3 s6

s7

T4

T2 T3 T6

T7

T8T1 T5

s4

s8

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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.

Our work focuses on composition selection wrt:both latter criteria!

Selection

SelectionSemantics based

QoS baseds1 s5

s3 s6

s7

T4

T2 T3 T6

T7

T8T1 T5

s4

s8

s12, s

22, s

32, ...

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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.

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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: sx

S Input Parameters

y

S Input Parameters

xS Output Parameters

y

S Output Parameters

x

Web service: sy

Out_sy In_sx

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

NetWorkConnectionNetWorkConnection

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

NetWorkConnectionSlowNetWorkConnection

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

SlowNetWorkConnectionNetWorkConnection

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

VoIPIdIPAddress

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

NetWorkConnectionAddress

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Extra & Common Description in Semantic Links

Definition (Concept Abduction)Let L be a DL, Out_si , In_sj be two concepts in L, and T be aset of axioms in L. A Concept Abduction Problem (CAP),denoted as 〈L, Out_si , In_sj , T 〉 is finding a concept H ∈ Lsuch that T |= Out_si u H v In_sj .

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 .

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Extra & Common Description in Semantic Links

Definition (Concept Abduction)Let L be a DL, Out_si , In_sj be two concepts in L, and T be aset of axioms in L. A Concept Abduction Problem (CAP),denoted as 〈L, Out_si , In_sj , T 〉 is finding a concept H ∈ Lsuch that T |= Out_si u H v In_sj .

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

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Extra & Common Description in Semantic Links

Definition (Concept Abduction)Let L be a DL, Out_si , In_sj be two concepts in L, and T be aset of axioms in L. A Concept Abduction Problem (CAP),denoted as 〈L, Out_si , In_sj , T 〉 is finding a concept H ∈ Lsuch that T |= Out_si u H v In_sj .

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

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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,Out_si ,In_sj ,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 ).

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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,Out_si ,In_sj ,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 ).

Web service: sxSemantic connection:

S Input Parameters

y

S Input Parameters

xS Output Parameters

y

S Output Parameters

x

Web service: sy

SimT

SlowNetWorkConnectionNetWorkConnection

(Subsume i.e., )12_

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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,Out_si ,In_sj ,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 ).

q(si) for Elementary Services si

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

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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,Out_si ,In_sj ,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 ).

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))

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

Connection

Slow

Output Parameter

Input Parameter

T: Task

s: Service

Network

Connection

Legend

Semantic Link slNetwork sl16,8

s1 s5

s2 s3 s6sl12,3 sl15,6

T2 T3 T6

T8T1 T5

sl11,2 sl13,5

s8

Sequence

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

Output Parameter

Input Parameter

T: Task

s: Service

Legend

Semantic Link sl

sl15,7

s5AND

Branching

s6

s7

sl15,6

T6

T7

T5

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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

Semantic Link sl

Connection

Slow

Output Parameter

Input Parameter

T: Task

s: Service

Network

Connection

Legend

Network

s1

s2

sl11,4T4

T2

T1

sl11,2

s4

OR-Branching

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

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))

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Web Service Composition Driven CSP

CSP FormalizationFormalization as a triple (T , D, C):

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 ∈ <+

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

with si,1≤i≤n ∈ Di,1≤i≤n;which satisfies all the constraints C.

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

A Stochastic Search Method (1)

PrinciplesSacrificing completness (i.e., all solutions) for speed;Based on a simple idea: computing “a single” solution.

Our ApproachAdaptation of the Hill Climbing algorithm.→ Appropriate for a large number of services.

S. Russell and P. Norvig.Artificial Intelligence: A Modern Approach.Ed. Prentice-Hall, 1995.

Computational ComplexityCSP based search methods: Exponential!Stochastic search methods (e.g., Hill Climbing) scale better!

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

A Stochastic Search Method (2)

RequirementsAn evaluation function f for each composition c:

f (c) =ωcd Q̂cd(c) + ωmQ̂m(c)

ωpr Q̂pr (c) + ωtQ̂t(c)

An adjacency function: c1 and c2 are adjacent to eachother if they differ in exactly one assignment (s, T ).

Algorithm in Details1) Let’s start with a random composition cfinal .2) f -Evaluation of all ci,1≤i≤n adjacent to cfinal .

If ∃i such that f (cfinal) ≤ f (ci) then f (cfinal)← f (ci).

3) Iteration until all constraints are satisfied by cfinal .4) If no solution, constraints relaxing.

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Evolution of Constraints SatisfactionThe more tasks, services the more time consuming!

Evolution of Composition QualityOptimal composition: High Time consuming!Compositions that satisfy constraints: More scalable!

Search Process vs. DL Reasoning (|T | > 100, |s| > 350)

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

Vs. State-of-the-art Approaches (T = 300 |s| > 280)

Adoption of stochastic search method for large domains!No exponential search required.

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Evolution of Constraints SatisfactionThe more tasks, services the more time consuming!

Evolution of Composition QualityOptimal composition: High Time consuming!Compositions that satisfy constraints: More scalable!

Search Process vs. DL Reasoning (|T | > 100, |s| > 350)

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

Vs. State-of-the-art Approaches (T = 300 |s| > 280)

Adoption of stochastic search method for large domains!No exponential search required.

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Evolution of Constraints SatisfactionThe more tasks, services the more time consuming!

Evolution of Composition QualityOptimal composition: High Time consuming!Compositions that satisfy constraints: More scalable!

Search Process vs. DL Reasoning (|T | > 100, |s| > 350)

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

Vs. State-of-the-art Approaches (T = 300 |s| > 280)

Adoption of stochastic search method for large domains!No exponential search required.

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Evolution of Constraints SatisfactionThe more tasks, services the more time consuming!

Evolution of Composition QualityOptimal composition: High Time consuming!Compositions that satisfy constraints: More scalable!

Search Process vs. DL Reasoning (|T | > 100, |s| > 350)

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

Vs. State-of-the-art Approaches (T = 300 |s| > 280)

Adoption of stochastic search method for large domains!No exponential search required.

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Quality-driven Semantic Web Service Composition[Theoretical]

A general and extensible model to evaluate compositions;Scalabilty: A solution rather than the most optimal.

CSP formalization.Adaptation of a stochastic search method.

[Experimental]Good computation costs despite the off-line DL reasoning.

Future WorkExtension with more composition constructs;Considering a finer abduction operator;Dynamic Distribution of the CSP on different peers;Focusing on a process that reduces the number ofsemantic links: Macro composition of Web services.

Introduction Related Work Background Quality Model A Scalable Approach Experimentation Conclusion

Question?