Multi-Agent Deep Reinforcement Learning-Based Fine-Grained Traffic Scheduling in Data Center Networks

Future Internet Pub Date : 2024-03-31 DOI:10.3390/fi16040119
Huiting Wang, Yazhi Liu, Wei Li, Zhigang Yang
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Abstract

In data center networks, when facing challenges such as traffic volatility, low resource utilization, and the difficulty of a single traffic scheduling strategy to meet demands, it is necessary to introduce intelligent traffic scheduling mechanisms to improve network resource utilization, optimize network performance, and adapt to the traffic scheduling requirements in a dynamic environment. This paper proposes a fine-grained traffic scheduling scheme based on multi-agent deep reinforcement learning (MAFS). This approach utilizes In-Band Network Telemetry to collect real-time network states on the programmable data plane, establishes the mapping relationship between real-time network state information and the forwarding efficiency on the control plane, and designs a multi-agent deep reinforcement learning algorithm to calculate the optimal routing strategy under the current network state. The experimental results demonstrate that compared to other traffic scheduling methods, MAFS can effectively enhance network throughput. It achieves a 1.2× better average throughput and achieves a 1.4–1.7× lower packet loss rate.
数据中心网络中基于多代理深度强化学习的细粒度流量调度
在数据中心网络中,面对流量波动大、资源利用率低、单一流量调度策略难以满足需求等挑战,有必要引入智能流量调度机制,提高网络资源利用率,优化网络性能,适应动态环境下的流量调度需求。本文提出了一种基于多代理深度强化学习(MAFS)的细粒度流量调度方案。该方法利用带内网络遥测技术收集可编程数据平面的实时网络状态,建立实时网络状态信息与控制平面转发效率之间的映射关系,并设计多代理深度强化学习算法计算当前网络状态下的最优路由策略。实验结果表明,与其他流量调度方法相比,MAFS 能有效提高网络吞吐量。其平均吞吐量提高了 1.2 倍,丢包率降低了 1.4-1.7 倍。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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