Self-adaptive fuzzy QoS-driven web service discovery

Due to the high proliferation of web services, selecting the best services from functional equivalent service providers have become a real challenge, where the quality of the services plays a crucial role. But quality is uncertain, therefore, several researchers have applied Fuzzy logic to address the imprecision of the quality of service (QoS) constraints. Furthermore, the service market is highly dynamic and competitive, where web services are constantly entering and exiting this market, and they are continually improving themselves due to the competition. Current fuzzy-based techniques are expert and/or consensus-based, and therefore too fragile, expensive, non-scalable and non-self-adaptive. In this paper we introduce a new methodology to support requesters in selecting Web services by automatically connecting imprecisely defined QoS constraints with overly precise service QoS offerings over the time. We address the dynamism of the market by using each time a modified fuzzy c-means module that allows providers to automatically organize themselves around the QoS levels. The advantage of our approach is that consumers can specify their QoS constraints without really knowing what are the current best quality ranges. We illustrate our approach with a case of study.