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Constraint Driven Web Service Composition in METEOR-S

Constraint Driven Web Service Composition in METEOR-S. Rohit Aggarwal, Kunal Verma, John Miller, Willie Milnor Large Scale Distributed Information Systems (LSDIS) Lab University of Georgia, Athens Presented By: Dr. Amit P. Sheth. Outline. Introduction METEOR-S @LSDIS

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Constraint Driven Web Service Composition in METEOR-S

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  1. Constraint Driven Web Service Composition in METEOR-S Rohit Aggarwal, Kunal Verma, John Miller, Willie Milnor Large Scale Distributed Information Systems (LSDIS) Lab University of Georgia, Athens Presented By: Dr. Amit P. Sheth

  2. Outline • Introduction • METEOR-S @LSDIS • Constraint Driven Composition • Conclusions

  3. Introduction • Currently businesses are statically bound to partners • Static business models • Technological constraints • Emerging business models require more dynamism • Web services allow inter platform application integration • New challenges • Create a more dynamic business process creation environment • Allow automatic integration of partners in business processes • Create tools for optimizing such dynamic processes

  4. Characterizing the challenges • How to automatically integrate services ? • Semantic Web Services • The plug-and-play feature wherein services can be selected and replaced automatically requires Web service Semantics • How to optimize processes? • Constraint Analysis • To be able to select Web services that are optimal and satisfy client’s constraints requires the use of a Constraint Analyzer/Optimizer

  5. This work … • Presents constraint based Web service composition in METEOR-S • Presents an approach to integrate dynamic binding and optimization with BPEL4WS • Presents an approach that combines constraint analysis using description logics with integer linear programming http://www.daml.org/services/use-cases/architecture/QosUseCase/DynamicQoSWebProc-SWSA-UseCase-v1.htm

  6. Outline • Introduction • METEOR-S @LSDIS • Constraint Driven Composition • Conclusions

  7. METEOR-S • Uses semantics in the entire life cycle of Semantic Web Services and Processes • Semantics in Annotation, Publication, Discovery and Composition of Web Services • Comprehensive use of semantics (Data, Functional/Operational, QoS and Execution/Runtime) • Integrates and co-exists with current industry technologies E.g. Eclipse BPWS4J Editor, BPEL4WS Execution Engine • Consistent with and builds upon current industry standards and recommendations

  8. uses query Ranked Response METEOR-S Back-End DesignTime Abstract Process Abstract Process Designer Executable Process Binder Process Repository BPWS4J Execution Engine Service Template(s) (PUBLISH) Process Instance Initiation Time Process Annotation Tool Optimized Service Set Constraint Analyzer Discovery Engine Enhanced UDDI

  9. Outline • Introduction • METEOR-S @LSDIS • Constraint Driven Composition • Conclusions

  10. Constraint Based Process Composition • User defines High level goals • Abstract BPEL process (control flow without actual service bindings ) • Process constraints on QoS parameters • Generic parameters like time, cost, reliability • Domain specific parameters like supplyTime • Domain constraints captured in ontologies • E.g preferred suppliers, technology constraints

  11. Sample Abstract BPEL Process DEFINITIONS <process name="orderProcess" targetNamespace="http://tempuri.org/" xmlns="http://schemas.xmlsoap.org/ws/2003/03/business-process/" xmlns:tns="http://tempuri.org/"> <partnerLinks> <partnerLink name="User" xmlns:ns1="tns" partnerLinkType="ns1:UserSLT"/> <partnerLink name="orderPartner" xmlns:ns2="?" partnerLinkType="ns2:?"/> <partnerLink name="orderPartner2" xmlns:ns8="?" partnerLinkType="ns8:?"/> </partnerLinks> <flow name="start"> <invoke name="orderPArt" partnerLink="orderPartner" xmlns:ns7="?" portType="ns7:?" operation="?" inputVariable="orderInput" outputVariable="orderOutput"> </invoke> <invoke name="orderPArt2" partnerLink="orderPartner2" xmlns:ns9="?" portType="ns9:?" operation="?" inputVariable="orderInput" outputVariable="orderOutput"> </invoke> </flow> </process> Unknown partners FLOW

  12. Constraint Analyzer/Optimizer • Constraints can be specified on each activity or on the process as a whole. • An objective function can also be specified e.g. minimize cost and supply-time etc • The Web service publishers provide constraints on the web services. • The constraint optimizer makes sure that the discovered services satisfy the client constraints and then optimizes the service sets according to the objective function.

  13. Constraint Representation – Domain Constraints

  14. Constraint Representation – Process Constraints

  15. Integer Linear Programming • Constraints are converted into linear equalities/linear inequalities over a set of discovered services. • We have used LINDO API which helps in solving ILP problems. e.g. if three services match the service template with a constraint that cost<=500 and minimum A + B + C = 2 (choose 2 services) CA*A + CB*B + CC*C <= 500 (total cost constraint) And minimize (CA*A + CB*B + CC*C) as objective function (where A, B and C are binary)

  16. Working of Constraint Analyzer Service Template 1 Service Template 2 Abstract ProcessSpecifications Process constraints Supply-time<=7 Cost<=400 Min (Cost, Supply-time) Supply-time <= 3 Cost <=300 Supply-time <= 4 Cost <=200 DiscoveryEngine ST=3 C=180 ST=4 C=200 ST=3 C=250 ST=2 C=100 Constraint Analyzer ST=1 C=300 ST=3 C=200 ST=3 C=180 ST=4 C=200 ST=3 C=250 ST=2 C=100 Objective FunctionMin (supply-time + cost) Ranked Set

  17. Outline • Introduction • METEOR-S @LSDIS • Constraint Driven Composition • Conclusions

  18. Conclusion • METEOR-S adds the advantage of taking an abstract process as a starting point and automatically binding services to it • To have dynamism in process composition • METEOR-S helps to provide the plug-and-play support for dynamically selecting Web services by enhancing discovery of relevant Web services using Semantics. • METEOR-S reduces manual intervention during Web process composition. It has the facility of choosing the optimal set automatically or having the user choose the best set from a list • Constraint analysis gives a better service and choice to the clients by making sure that the services satisfy the constraints and also by choosing the optimal set of services automatically.

  19. References • [Rajasekaran et al., 2004] P. Rajasekaran, J. Miller, K. Verma, A. Sheth, Enhancing Web Services Description and Discovery to Facilitate Composition, Proceedings of SWSWPC, 2004 • [METEOR-S, 2002] METEOR-S: Semantic Web Services and Processes, http://swp.semanticweb.org , 2002. • [Ankolenkar et al., 2003] The DAML Services Coalition, DAML-S: Web Service Description for the Semantic Web, The First International Semantic Web Conference -ISWC, Italy • [Roman et al., 2004] D.Roman, U. Keller, H. Lausen, WSMO – Web Service Modeling Ontology (WSMO), DERI Working Draft 14 February 2004, http://www.wsmo.org/2004/d2/v0.1/20040214/

  20. Thank You http://lsdis.cs.uga.edu/Projects/METEOR-S/

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