QoS-aware cloud service composition: A systematic mapping study from the perspective of computational intelligence

Abstract

Cloud service composition builds new value-added services by combining existing single services. However, because of the exuberant growth of cloud services and the varying quality of service (QoS), discovering required services and creating a service composition with certain quality guarantees becomes a significant technical issue and attracts much concern. Computational intelligence techniques are considered to be effective in solving such problems, and researchers have made substantial efforts in this area. Nevertheless, to the best of our knowledge, there is not any systematic research about this issue with a particular focus on computing intelligence. Thus, the current study aims to create a panoramic view of QoS-aware cloud service composition from the perspective of computational intelligence. The objectives of this paper are to (1) investigate the relevant studies on this field; (2) make a comprehensive examination of the literature from different aspects: active researchers, research motivations, QoS parameters, algorithms, datasets, optimization strategies and could layers; (3) identify the areas which need further research. For this, a search protocol has been well defined, and 105 articles from 2009 to 2018 were selected. This study classified these articles into three groups, including non-heuristic, heuristic and meta-heuristic, and then examined the research works from several aspects. The results indicate that reducing response time is the most important motivation for researchers, and meta-heuristic algorithms, especially genetic algorithms, are the most widely used computational intelligence techniques. Besides, the most widely used QoS attributes and datasets are also revealed. Additionally, the study points out there are still some research challenges in this area, such as QoS evaluation in a dynamic environment and inter-service correlations. In general, this study classified and compared the existing computational intelligence techniques; analyzed the research status and identified future research directions. It can provide a basis for both researchers and practitioners who are interested in this area. More significantly, in the field of expert and intelligent systems, this study can assist in the design and development of expert and intelligent systems in enterprises, it can efficiently assist enterprises in business decisions and risk reduction.