An efficient two-phase approach for reliable collaboration-aware service composition in cloud manufacturing

Composing existing services into one value-added composite service for a cooperative business process is especially attended by both academia and industry in the cloud manufacturing (CMF) environment from the perspective of industrial information integration. Various service composition methods based on the QoS have been proposed to satisfy the user's requirement. However, the traditional methods fail to consider interrelation among various services and change in QoS. The unstable QoS may affect the reliability of service composition, which may also affect reliable collaboration as cloud services need to cooperate with each other to accomplish the business process efficiently. In this paper, we propose an efficient two-phase approach by integrating clustering and Chaos-Gauss-based PSO to solve the above problem. In phase one, the K-means clustering algorithm is adopted to improve the quality of candidate services with the consideration of QoS stability for reducing the search space. In phase two, a novel multi-objective PSO algorithm based on Chaos-Gauss named CGPSO is proposed to find the optimal service composition. The Two-Phase approach considers both the QoS stability and service collaboration ability to reduce the probability of service composition failure. Further, we conduct a comprehensive analytical and experimental study to show that our approach has better performance and effectiveness for service composition than other approaches in the CMF environment.