QoS decomposition for service composition using genetic algorithm

Recently, a lot of research has been dedicated to optimizing the QoS-aware service composition. This aims at selecting the optimal composed service from all possible service combinations regarding user's end-to-end quality requirements. Existing solutions often employ the global optimization approach, which does not show promising performance. Also, the complexity of such methods extensively depends on the number of available web-services, which continuously increase along with the growth of the Internet. Besides, the local optimization approaches have been rarely utilized, since they may violate the global constraints. In this paper, we propose a top-down structure, named quality constraints decomposition (QCD) here, to decompose the global constraints into the local constraints, using the genetic algorithm (GA). Then the best web service for each task is selected through a simple linear search. In contrast to existing methods, the QCD approach mainly depends on a limited set of tasks, which is considerably less complex, especially in the case of dynamically distributed service composition. Experimental results, based on a well-known data set of web services (QWSs), show the advantages of the QCD method in terms of computation time, considering the number of web services.