Uncertainty-aware adaptive service composition in cloud computing

Cloud computing service composition is to select appropriate component services from numerous of services distributed in different clouds to build scalable loose coupling value-added applications. Traditional service composition methods are usually divided into selection stage and composition stage. Hardly guaranteeing the services with the best performance in the selection stage are still optimal in the execution stage because of the dynamic nature of the cloud computing environment and the stochastic nature of services evolution. Focusing on these two natures of service composition in cloud computing environment, a service composition model is built based on POMDP (partially observable Markov decision process) named as SC_POMDP (service composition based on POMDP), and a Q-learning algorithm is designed to solve the model. SC_POMDP can dynamically select the component services with outstanding QoS (quality of service) during the execution of service composition, which aims to ensure the adaptability of the service composition. Different from most existing methods, the proposed SC_POMDP regards the environment of service composition as being uncertain, and the compatibility between component services is considered, hence SC_POMDP is more in line with the real situation. Simulation experiments demonstrate that the proposed method can successfully solve the problems of service composition in different sizes. Specially, when service failure occurs, SC_POMDP can still select the optimal alternative component services to ensure the successful execution of the composite service. Compared with two existing methods, the selected composite service by SC_POMDP is best in response time and throughput, which reflects the superior adaptation of SC_POMDP. © 2016, Science Press. All right reserved.