Services Selection of QoS-based Skyline Computation for Web Service Composition

Abstract

Nowadays, the exploding number of functionally similar Web services has led to a new challenge of selecting the most relevant services using quality of service (QoS) aspects. Traditionally, the relevance of a service is determined by computing an overall score that aggregates individual QoS values. Users are required to assign weights to QoS attributes. This is a rather demanding task and an imprecise specification of the weights could result in missing some user desired services. Recent approaches focus on computing service skyline over a set of QoS aspects. This can completely free users from assigning weights to QoS attributes. In this paper we use skyline computation to select services for composition effectively and efficiently, reducing the number of candidate services to be considered. We also define aggregation functions, and use a Multiple Attribute Decision Making approach for the utility function to achieve Qos-based optimal service selection. Proofs are also presented. We evaluate our approach experimentally using both real and synthetically generated datasets.