Refining the Skyline with fuzzy similariy measures and Topsis method for the optimization of web services composition
The emergence of new paradigms in information technology environments has lead to a dramatic growth in the amount of available web services. This raises many challenges in service computing and specifically in service composition problems. 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) However, proceeding in an exhaustive search is very time consuming and cannot be adopted in large scale systems. 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 based on dominance relationships. This paper deals with some of the issues of the Skyline computation. In fact, used dominance relationships may suffer from looseness and data sparsity. Hence, the size of the Skyline may still be too large and cannot optimize service composition. We propose a new approach to overcome those problems by refining the Skyline with fuzzy techniques inspired by the Topsis method. Experimental evaluation demonstrates the effectiveness of the proposed concept and the efficiency of our implementation.
2016 IEEE International Conference on Fuzzy Systems (FUZZ IEEE)