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 Constraint Driven Web Service Composition in METEOR-S

Outline Introduction Constraint Driven Composition Conclusions

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

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

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

Outline Introduction Constraint Driven Composition Conclusions

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 EngineEclipse BPWS4J Editor BPEL4WS Execution Engine Consistent with and builds upon current industry standards and recommendations

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

Outline Introduction Constraint Driven Composition Conclusions

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

Sample Abstract BPEL Process <process name="orderProcess" targetNamespace=" xmlns=" xmlns:tns=" DEFINITIONS Unknown partners FLOW

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.

Constraint Representation – Domain Constraints FactOWL expression Supplier1 is an instance of network adaptor supplier Supplier1 supplies #Type1 Supplier1 is a preferred supplier. preferred Type1 is an instance of NetworkAdaptor Type1 works with Type1Battery

Constraint Representation – Process Constraints FeatureGoalValueUnitAggregation CostOptimizeDollars Σ (minimize total process cost) supplytimeSatisfy< 7Days MAX (Max. supply time below Value) partnerStatusOptimize MIN (Select best partner level; lower value for preferred partner)

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) C A *A + C B *B + C C *C <= 500 (total cost constraint) And minimize (C A *A + C B *B + C C *C) as objective function (where A, B and C are binary)

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

Outline Introduction Constraint Driven Composition Conclusions

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.

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, 2004Enhancing Web Services Description and Discovery to Facilitate Composition [METEOR-S, 2002] METEOR-S: Semantic Web Services and Processes, [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,

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