The Ecological Model and Global optimization Algorithm for Service Internet

Abstract

With the further development of service Internet, how to effectively manage, control and optimize the entire service Internet system has become a research hotspot. However, the current research on service Internet optimization mostly focuses on service selection and service composition. And few studies have provided the global optimization strategy for service Internet. So, in this paper, first of all, we propose an ecological model of service Internet by using ecological theory and business process characteristics. Then, based on this model, considering the time-varying uncertainty of service demands, a global service Internet optimization policy learning algorithm called GSIOA is proposed, which uses deep reinforcement learning to automatically formulate global management, control, and optimization strategies of service Internet. Finally, through the comparison of simulation results, it can be concluded that the performance of GSIOA is better than other baseline methods. Our algorithm based on deep reinforcement learning can well solve the global optimization problem of service Internet.