Energy Consumption Optimization of Edge Computing Based on Reinforcement Learning

Abstract

Abstract Cloud computing faces the problem of over-centralization, where the distance between users and cloud servers is very far. The communication between users and clouds faces long communication latency and high computational energy consumption. Edge computing is a hot research topic in academia now. Edge computing sinks cloud computing nodes to the edge, allowing users to offload tasks directly to edge servers. Compared with cloud computing, edge computing is closer to the user side. The communication between users and edge servers has lower transmission latency and less energy consumption. In order to better exploit the features and promote the development of edge computing, in this paper, we mainly focus on exploring the basic architecture of edge computing. In addition, we propose a scheme that can optimize the energy consumption of edge computing based on reinforcement learning methods. Finally, we verify the effectiveness of our proposed scheme by comparing it with other schemes through simulation experiments.