Dynamic Session Management Based on Reinforcement Learning in Virtual Server Environment

In a virtualized server environment, machine resources such as CPU and memory are shared by multiple services. In such an environment, as the number of sessions for each service increases, the amount of resources that are utilized by the services increases. If thrashing occurs due to a lack of resources, the performance of the server is degraded. It is effective to estimate the amount of used resources; however, it is hard to estimate the amount of resources that are used dynamically by multiple ser-vices. In this paper, the authors propose a dynamic session management based on reinforcement learning in order to utilize the resources effectively and avoid the thrashing.