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Talk @ ICWS 2010
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Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
SOA4All: An Innovative IntegratedApproach to Services Composition
Freddy Lécué1 Yosu Gorronogoitia2 Rafael Gonzalez2
Mateusz Radzimski2 Matteo Villa3
firstname.lastname@{manchester.ac.uk,atosresearch.eu, txt.it}
1The University of Manchester, Booth Street East, Manchester, UK2ATOS Research, Albarracín 25, Madrid, Spain
3TXT e-Solutions SpA, Via Frigia 27, Milan, Italy
IEEE 8th International Conference on Web ServicesJuly 5th - 10th, 2010
Miami, FL, USA
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Outline
1 Introduction
2 An Integrated Approach for Service Composition
3 Architecture Components
4 Experimental Results
5 Related Work
6 Conclusion
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Big Picture(Semi-automation of) service composition in the semantic web.
Here we address ...Semi-automated generation of optimal compositions wrt:
Properly formulated user requirements e.g., preferences,constraints, goal;On-demand generation of templates (or schema) basedcomposition.
So ...Compositions are not elaborated from scratch!... but rely on an end-to-end architecture of integratedcomponents: Template Generator, Composer andOptimizer.
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Web service, Semantic Web and Semantic Web Services
Nowadays Web: syntax-based Web.Semantic Web is an extension of current Web in whichinformation is given well-defined meaning.
Ontology: a key enabling technology (RDF, OWL)Semantic web principles applied to web services
Give a semantics to services description;Description languages with a semantics;
1
Static URI, HTML, HTTPWWW Semantic Web
RDF, RDF(S), OWL
Semantics
DynamicUDDI, WSDL, SOAPWeb Services Intelligent Web
Services
Bringing the web to its full potential SAWSDL, OWL-S, WSMO ...
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
General Overview
Integration of 3 Main ComponentsService Template Generator, Composer and Optimizer
Reasoning
(ProM APIs)
Template
Generator
Services
JGAP)(Genetic Algorithms
Optimizer
Compositions
RelevantServices
Services Description
based Approach)(Parametric Design
Composer
Design−Time
Relevant
Semantic
Services Discovery
Ex
ecu
tion
Execution Logs
Abstract Activities
Repository of Compositions with
End User Constraintsand Preferences
Not Found
The end−user picks a
template that fulfills
her goals
Binding to ActivitiesConcrete Service
Compositions with
(Active BPEL)
(Fact++)
Atomic ServicesBPEL4SWS
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
General Overview
Integration of 3 Main ComponentsService Template Generator, Composer and Optimizer
RelevantServices
Services DescriptionSemantic−based
Compositions
Optimizer
(Genetic AlgorithmsJGAP)
Services
Monitoring and
Management
Infrastructure
Generator
Template
(ProM APIs)
Reasoning
Semantic
Relevant
Design−Time
Composer
(Parametric Designbased Approach)
Links([LecL06])(Fact++)
Ex
ecu
tion
Execution Logs
Abstract Activities
Repository of Compositions with
End User Constraintsand Preferences
Not Found
The end−user picks a
template that fulfills
[PedLMLDK10])(RDF Repository
her goals
Binding to ActivitiesConcrete Service
Compositions with
Atomic ServicesBPEL4SWS
Services Discovery ([JunAS10])
(Active BPEL)
Semantic
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Integrated Approach: An Example
ContextUtilizing the cloud to expose (semantic web) services (with their nonfunctional properties) on product information (from differentcompanies) e.g., retrieving a product list, ordering a specific product.
GoalAggregation and classification of products from different service (e.g.,getProductData, getProductURI, getProductPrice) providers.
ProductData ≡ ∀hasID.ProductID u∀hasName.ProductNameu∀hasURI.URI
ProductURI ≡ ∀hasURI.URI
Product ≡ ∀hasID.ProductID u ∀hasName.ProductName
∀hasURI.URI u ∀hasPrice.ProductPrice
EuropeanProduct ≡ Product u ∀hasProvider .Europe
ProductIDList ≡ ∀hasID.ProductID
ProductID v >, ProductPrice v >, Europe v >
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Template Generation (1)
IssueDesigning complex compositions: a difficult task for end-users.
ApproachContextual-driven filteringapproach to discover relevantpast executions (logs) .Process mining-orientedtechniques based on logs.Semantic Reasoning onservices descriptions.Discovery of possible templatesand their hierarchy.
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Template Generation (2)
ResultsA selection of templates presented to end-users.
AdvantagesComposition of activities/services: Not from scratch!Collaborative generation of templates.Customized configuration of templates, depending on theabstraction level.
based Approach)
Generator
Template
(ProM APIs)
Composer
(Parametric Design
Monitoring and
Management
Infrastructure
Compositions
Optimizer
(Genetic AlgorithmsJGAP)
Design−Time
Services Discovery ([JunAS10])
(Active BPEL)Exec
uti
on
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Template Generation: An Example
ExampleAn E-Commerce template satisfying the goal: Aggregation andclassification of products of different service providers.
ANDby Provider AGetProduct
AbstractTask AbstractTask
AggregateProductfor Provider A
by Provider BGetProduct
AbstractTask
by Provider B
AbstractTask
ClassifyProduct
TaskImplicit Data Flow between Abstract Tasks of the Composition End NodeStart Node
Branching
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Parametric Design Based Composition (1)
IssueGoal-based, semi-assisted, and context-aware service composition.
ApproachAdaptable concretizationtransformation using Multi-AgentSystems e.g., adding newontologies, services andtemplates.
Approach (Cont.)Elaboration of servicesbinding and data flow from anincomplete specification of acomposition.
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Parametric Design Based Composition (2)
ResultAn executable composition of web services.
AdvantagesComposition problem defined by domain-specificsub-problems.Adaptable and flexible version to achieve composition.Knowledge-intensive configuration procedure.
Generator
Template
(ProM APIs)
Design−Time
Composer
(Parametric Designbased Approach)
Optimizer
(Genetic AlgorithmsJGAP)
Monitoring and
Management
Infrastructure
Compositions
Services Discovery ([JunAS10])
(Active BPEL)Exec
uti
on
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Parametric Design Based Composition: An Example
Example
Concretization of an E-Commerce template satisfying: Aggregationand classification of products of different service providers.
Semantic Link sl
by Provider AGetProduct
AbstractTask AbstractTask
AggregateProductfor Provider A
by Provider BGetProduct
AbstractTask
by Provider B
AbstractTask
ClassifyProduct
getClothingProductIDList
aggregateClothingProductPriceData
getFootwearProductPrice
Implicit Data Flow between Abstract Tasks of the Composition Start NodeTask/Service
Output ParameterInput Parameter
BranchingAND
ProductIDList
ProductURI
ProductPrice
getFootwear
EuropeanProduct
ProductID
ProductID
ProductIDList ProductPriceDatagetFootwear
BranchingAND
ProductURI
classifyEUFootwear
End Node
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Optimization (1)
IssueHow select the most optimal among some achieving a same goal?
ApproachCriteria: Semantic and non functional properties of services.Multi-objective optimization: Genetic algorithm-based approach.
Selection
SelectionSemantics based
QoS baseds1 s5
s3 s6
s7
T4
T2 T3 T6
T7
T8T1 T5
s4
s8
s12, s
22, s
32, ...
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Optimization (2)
ResultAn optimal composition of services on complementary dimensionsi.e., semantic and non functional.
Advantages
A general and extensible model to evaluate compositions.
CSOP (Constraints Satisfaction Optimization Problem)formalization.
Scalability of the Genetic algorithm-based approach despite theoff-line DL reasoning.
Generator
Template
(ProM APIs)
Design−Time
Composer
(Parametric Designbased Approach)
Compositions
Optimizer
(Genetic AlgorithmsJGAP)
Monitoring and
Management
Infrastructure
Services Discovery ([JunAS10])
(Active BPEL)Exec
uti
on
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Optimization: An Example
Example
Optimization of an E-Commerce composition satisfying: Aggregationand classification of products of different service providers.
ProductPriceData
Service Updated by the Composition Optimizer
getClothingProductIDList
aggregateClothingProductPriceData
ServiceOutput ParameterInput Parameter
getFootwearProductPrice
ProductData
getFootwearProductPrice
ProductID
ProductIDList AND
getClothing
ProductPrice
ProductIDList
ProductID
Product
Semantic Link sl End NodeStart Node
ProductPrice
ProductIDList
getFootwear
ProductPriceDatagetFootwear
ProductID
ProductID
ProductIDList BranchingAND
ProductURI
ProductURI
EuropeanProduct
classifyEUFootwear
getFootwearProductData
classifyFootwearProductPriceData
getFootwearBranching
ProductIDListaggregateClothing
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Scalability of the Architecture
ContextUp to 200 users u design compositions, up to 25 services s each:(10u, 5s), (50u, 10s), (100u, 15s), (150u, 20s), (20u, 25s).
1 10
100 1000
10000 100000 1e+06 1e+07 1e+08 1e+09
Case 110u;5s
Case 250u;10s
Case 3100u;15s
Case 4 150u;20s
Case 5 200u;25s
Avg
. Tim
es (
ms)
in L
ogar
ithm
Sca
le
Case i: Users u;Services s
From aTemplate of 2 act. 4 act. 7 act. 10 act. 12 act.
Templates GenerationCompositionOptimization
Main Results
Template Generator: Number of logs, users and services.
Composer/Optimizer: Number of activities and services.
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Integration in the EU SOA4All project Architecture
Part of a SOA4All: Service-oriented Architecture for Allhttp://www.soa4all.eu/, http://coconut.tie.nl:8080/dashboard/
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
Related Work
Template Generator
R. Agrawal, D. Gunopulos, F. Leymann,Mining process models from workflow logsIn EDBT, pages 469-483, ??, ??, 1998
Design Time Composer
P. Traverso and M. PistoreAutomated Composition of Semantic Web Services into Executable ProcessesIn ISWC, pages 380–394, Hiroshima, Japan, 2004.
Optimizer
L. Zeng, B. Benatallah, A.H.H. Ngu, M. Dumas, J. Kalagnanam, and H. Chang.A Qos-aware middleware for web services compositionIEEE Trans. Software Eng., 30(5):311-327, 2004
As an End-to-End Integration
P. Bertoli and J. Hoffmann and F. Lécué and M. PistoreIntegrating Discovery and Automated CompositionIn ICWS, pages 815-822, Salt Lake City, USA, July 2007.
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
An Integrated Approach to Services CompositionEnd-to-end composition and optimization:
Template Generator: Not to start from scratch.Composer: Further details to the generated templates.Optimizer: Maximization of the quality of compositions.
→ Validating in a realistic setting.
Future WorkScalability issues: Template Generation:
Context-based filtering to improve relevance of logs.Semantic-based mining for more relevant abstract process.
Adapting our architecture to contextual information.
Introduction Integrated Approach Architecture Components Experimentation Related Work Conclusion
An Integrated Approach to Services CompositionEnd-to-end composition and optimization:
Template Generator: Not to start from scratch.Composer: Further details to the generated templates.Optimizer: Maximization of the quality of compositions.
→ Validating in a realistic setting.
Future WorkScalability issues: Template Generation:
Context-based filtering to improve relevance of logs.Semantic-based mining for more relevant abstract process.
Adapting our architecture to contextual information.
Thanks for your attention!Freddy Lécué [email protected]
http://www.soa4all.eu/, http://coconut.tie.nl:8080/dashboard/