GACRM: A dynamic multi-Attribute decision making approach to large-Scale Web service composition

Abstract The problem of QoS-aware Web service composition (QWSC), i.e., how to select from a pool of candidate services to construct a composite service with the best overall QoS performance, is an NP-hard problem. To address a large-scale QWSC problem, a novel method is proposed based on information theory, multi-attribute decision making (MADM) and genetic algorithm. To capture complex judgments, the QWSC problem is formulated into a MADM representation which aims to find acceptable solutions assessed by multiple QoS attributes with varying distributions. To solve the MADM problem for QWSC, each QoS attribute is weighted in both a priori , subjective perspective and a posteriori , information-based perspective based on the discriminative capability of QoS attributes for a dynamic pool of candidate services. Furthermore, to solve the large-scale QWSC problem that conventional MADM methods cannot navigate, we develop a GACRM algorithm by integrating genetic algorithm (GA) with Compromise Ratio Method (CRM). Experiments demonstrate that GACRM obtains nearly the same solution ranking by the CRM but scales much better in terms of computation time for large-scale QWSC problems.