Executive Summary

Service Oriented Computing (SOC) is increasingly gaining momentum and offering an attractive paradigm for delivering variant functionalities to individuals and enterprises. However, service users are overloaded by a large number of candidate services offering similar functionalities. Therefore, selection of appropriate services fulfilling users’ requirements becomes a key challenge in the SOC domain. QoS-aware Web Service Selection (WSS) as complex problem solver has become one of the most highlighted issues in service computing area. The QoS-aware selection problem maps to multi-objective optimization problem that is classified as NP-hard problem. It consists of selecting the best candidate services that maximizes QoS metrics and adhere to the constraints of users. There is a large body of research covering different aspects of QoS-aware service selection. Despite considerable research efforts, numerous open issues have not been addressed. This research tries to open a new horizon for service selection to utilize collaborative decision support models. The overall goal is to investigate a Pareto-optimal model considering user’s objectives and constraints and propose a strategy to find the best trade-off of requested QoS metrics. Towards this end, two research aims are proposed: 1) To investigate the power of crowdsourcing and consensus theory to support QoS-aware service assessment. 2) To examine the role of fuzzy inference system to support multi-criteria service selection under vague preference of users. The proposed system should rank the candidate services with respect to their evaluated QoS metrics and user preferences on those metrics.