Efficient Skyline computation for optimal service composition with fuzzy preference relationships

Service composition is arising as an effective process to ensure the integration of multiple atomic web services in order to create customized composite web services. However, the exploding number of the deployed service candidates that is constantly increasing makes the process of choosing the best service candidates an important challenge. When there are multiple web services that offer the same functionalities, we need to select the best one according to its non-functional criteria (e.g. response time, price, reliability). Skyline is a technique that helps reducing the size of our search space and comes as a complementary approach to the optimization methods. In fact, Skyline consists in preselecting the best candidates in the search space according to their non-functional criteria. Most of the current Skyline computation relies on a strict dominance relationship called Pareto-dominance. In this paper, we propose a new method to compute the Skyline points with a fuzzy approach which allows taking into consideration the users preferences. We will through this paper show how we could construct a consistent fuzzy model to overcome the limitations of web service composition computation. Experimental evaluation demonstrates the effectiveness of the proposed concept and the efficiency of our implementation.