Web Services Composition Optimization Based on Semi-Markov Decision Process

Web service which is a kind of component on the Internet shows good encapsulation, loosely coupled and cross-platform. However, the single Web service has inevitable limitations in functionality; it can’t provide people with more function and complex service. In order to reduce the cost and time of developing, and achieve services value-added and reuse, we need to combine existing services to form a combination of services which can satisfy user’s demand. There are a lot of services with the same functions in the process of service composition; one of the key issues is to select service by using Qos (Quality of Service). The response time is a significant factor in the process of Web service composition; it is affected by network load and service itself. It is modeled by using finite state continuous time Semi-Markov Decision Process (SMDP), and then Q learning algorithm is used to obtain the optimal service composition policy.