Web Service Compositon Optimization Methods Under Constraint Based On Associate Petri Net

Along with triple play service delivery develop and Web standard gradually unified, a new service-oriented computational model appears, Web services composition as a new software service form, obtained the high speed development and progress, caused the scientific community and the business world attention. With the fast development of network technology, a new service-oriented computational model appears, which combines Web technology and software engineer technology together. Accordingly, Service-oriented Architectures (SOA) come into being. SOA is characteristic of service composition and lays stress on the importance of service so that application programs can work together to provide abundant and purposeful business flows and truly reflect business models. At present, Web service composition has become a hot researching field, because it can make good use of resources and thus has a promising future. In spite of the advantages of Web service composition, many issues remain to be solved in this field; compositon optimization under constraint is one of them.Composition is the maximum difference between traditional software and services, through composition can realize service resources efficient utilization and reuse, the method of composition determine the correctness of the service composition, the algorithm optimizing of composition determine the efficiency of the service composition. This paper puts forward the associate Petri net to modeling Web service composition under constraint, and research a number of advanced optimizing algorithm of Web service composition under constraint.In this article we introduce the concept of Web service, the concept of composition relation and the all world research's present situation at first. Secondly, we models the Web service composition under constrain by the associate Petri net, and gives a genetic algorithm to optimize the Web service composition under constrain. Next, through the analysis, we finds that the genetic algorithm in the optimization has some shortages, so we use the differential evolution algorithm to analyze according to the characteristics of the problem and use the algorithm to find the optimal composition. The simulation experiment shows that the DE&APN method is better than the GA&APN method. The former saves the time and has a higher ratio of feasible solution.