Research on Reinforcement Learning-Based Dynamic Power Management for Edge Data Center

Abstract

Mobile Edge Computing (MEC) is a supplement to traditional cloud computing. Its characteristics are low latency and high reliability, and it will be widely used in the future. However, their dense deployment pattern raises a big concern on the system-wide energy consumption. Dynamic power management (DPM) method is an important method to solve energy consumption problems, it saves energy by shutting down servers in the EDC that are idle or have low utilization. In this paper, a DPM method based on reinforcement learning was proposed, it achieves the trade-off between EDC service performance and energy consumption by learning the global optimal dynamic timeout threshold power management strategy by trial and error. Experiments have shown that the proposed method saves no less than 6.35% energy consumption compared to the expert-based method.