Deep Reinforcement Learning-Based Routing Optimization Algorithm for Edge Data Center

Abstract

Mobile Edge Computing (MEC) has solved a sharp increase in data volume caused by various emerging network applications. The edge data center is an essential part of MEC, which connects the edge of the network and the backbone network. Faced with a complex network environment, edge data centers suffer low bandwidth resource utilization and high network latency. This paper proposes Twin Delayed Deep Deterministic policy gradient based Routing Optimization (TRO) algorithm to improve the performance of edge data centers. The TRO algorithm uses Deep Reinforcement Learning (DRL) and Software-Defined Networking (SDN) to achieve routing optimization from two aspects of bandwidth utilization and load balancing. Experiments demonstrate that compared with other algorithms, the TRO algorithm proposed in this paper significantly improves network throughput and reduces average packet latency and average packet latency error.