AN OPTIMAL CONSTRAINT BASED WEB SERVICE COMPOSITION USING INTELLIGENT BACKTRACKING
Keywords:
Web service, Web Service Composition, Service Composition graph, Intelligent Backtracking, Optimization, Log File, dynamic mediation, service provider, value orderingAbstract
Composition of web services involves a complex task of analyzing the various services available and deducing the most optimal solution from the list of service sequences. The web services are viewed in the form of layers interlinked with each other based on some conditions to form a service composition graph dynamically. Layering of the web services is done based on a sequential arrangement of the services as designed by the web service provider. From the numerous service sequences available, the most optimal service is computed dynamically from the start to end of a web service composition. The optimal solution set, consisting of a number of services, is deduced as the path that has the least total weight from start to end of the service composition. The anomalies that might arise in the search for optimal solution are solved using the Intelligent Backtracking technique thereby eliminating any absurd problems. The idea of Intelligent Backtracking is to make the optimization more efficacious. Dependency-directed backtracking is used so that the past transaction records are saved, making it easier to track the flow of web service selection. A Log file concept is introduced to keep a record of the service transactions at each level in order to satisfy user constraints in the best possible way. In case the user constraints are not feasible enough to complete the composition of services, then based on the data in the log files, negotiation can be done with the user for the reselection of certain anomalous web services. Negotiation is a process of dynamic mediation with the user in case his requirements constraints cannot be satisfied with the list of services provided by the service provider. This concept, if put to use will be revolutionary as it not only helps achieve optimization but also enriches the QoS constraints for user satisfaction.
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