A Multiple Objectives Optimization Approach for QoS-Based Web Services Compositions

While a lot of Web Services are available nowadays, Quality-of-Service (QoS) becomes a crucial concern to distinguish alternatives from each other. QoS-based service composition aims to determining optimal combinations among exponential candidates with consideration of overall non-functional performance. In this paper, a multiple objectives optimization approach is proposed for the decision-making process. First, we design a pattern-wise replacement method to derive composite QoS objectives of a given XML-encoded workflow process. According to the process descriptions, the composite QoS objectives will be available by synthesizing non-functional metrics of primitive patterns repeatedly. Second, we employ the epsiv-Pareto dominance relations to discriminate the overall QoS performance of combinations. Thus user can freely set any desirable epsiv values to define the precision of service composition's QoS performance. Finally, we present a genetic algorithm to find out the optimal combinations through evolutionary computation. The experimental results have shown that the proposed approach is more efficient and effective in terms of user-defined epsiv values for different QoS objectives.