Composing Distributed Data-Intensive Web Services Using Distance-Guided Memetic Algorithm

Abstract

Web services are fundamental elements of distributed computing and allow rapid development of distributed applications. Data-intensive Web services handle an enormous amount of data created by different companies. Data-intensive Web service compositions (DWSC) must fulfil functional requirements and optimise Quality of Service (QoS) attributes, simultaneously. Evolutionary Computing (EC) techniques allow for the creation of compositions that meets both requirements. However, current approaches to Web service composition have overlooked the impact of data transmission and the distribution of services, rendering them ineffective when applied to distributed data-intensive Web service composition DWSC. Especially, those approaches failed to consider important information from the problem that enables us to quickly determine the suitability of any solution. In this paper, we propose an EC-based algorithm with novel crossover operators to effectively address the above challenges. An evaluation is carried out and the results show that our proposed method is more effective than the existing methods.