A Hybrid MCDM Framework for Efficient Web Services Selection Based on QoS

With the rapid development of Cloud Computing and Service Oriented Computing, the processes of selecting web services which gives the same functionality with different quality of service (QoS) become an important issue. To deal with the large number of candidates, Skyline method is used frequently to find the most pertinent Web services that are not dominated by any other service; whenever, i) the number of Skyline Web Services cannot be controlled. ii) Skyline doesn't allow assigning importance weights to QoS attributes. In this paper we propose an efficient framework to handle the above drawbacks. K-representative Skyline is used to reduce the research space giving users a summary about the full Skyline Web Services. For weighting QoS attributes we propose an enhanced version of Fuzzy AHP method based on natural language and asking fewer efforts to users. To Rank-order pertinent Skyline Web Services we adapt an improved version of Promethee leveraging the outranking relationships between every Skyline Web services. The experimental evaluation performed on QWS dataset illustrates that our framework can better elicitate the user preferences and retrieve the best ranked K-Representative Skyline Web Services.