基于深度强化学习的边缘数据中心路由优化算法

Jixin Zhao, Shukui Zhang, Yang Zhang, Li Zhang, Hao Long
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引用次数: 1

摘要

移动边缘计算(MEC)解决了各种新兴网络应用带来的数据量急剧增加的问题。边缘数据中心是MEC的重要组成部分,它连接着网络的边缘和骨干网。面对复杂的网络环境,边缘数据中心带宽资源利用率低,网络时延高。为了提高边缘数据中心的性能,提出了基于双延迟深度确定性策略梯度的路由优化算法。TRO算法采用DRL (Deep Reinforcement Learning)和SDN (software defined Networking)技术,从带宽利用率和负载均衡两个方面实现路由优化。实验表明,与其他算法相比,本文提出的TRO算法显著提高了网络吞吐量,降低了平均数据包延迟和平均数据包延迟误差。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep Reinforcement Learning-Based Routing Optimization Algorithm for Edge Data Center
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.
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