Maintaining high-performance operation under dynamic and nonuniform network traffic has been a technical challenge in current data center networks (DCNs). With the aim to provide better quality of service (QoS) for diverse applications, this work presents a dynamic and adaptive DCN reconfiguration framework based on deep reinforcement learning (DCR2L). The proposed framework is integrated into the SDN control plane of the DCN, implementing real-time and automatic DCN reconfiguration. Performance of the DCR2L framework is experimentally demonstrated in our DCN lab, including 4 racks and 16 servers. Experimental results show a network latency improvement of 6.9% based on the DCR2L at an average network bandwidth of 2.3 Gb/s. Based on measured traffic and physical parameters in the experiment, performance of the DCR2L framework is numerically assessed with the realistic traffic of diverse QoS requirements for both electrical and optical DCNs and for different data center scales. Leaf–spine electrical and OPSquare optical networks are set up in an OMNeT++ simulator. For a data plane network consisting of 16 racks and 320 servers, results indicate that the DCR2L framework improves the network latency of up to 16.4% under leaf–spine and up to 24.6% under OPSquare for the overall traffic with respect to the classical heuristic method. For a DCN scale of 10,240 servers, the DCR2L framework provides up to 12.5% lower latency for the leaf–spine electrical network and up to 17.8% latency improvement for the OPSquare optical network.

中文翻译：

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基于深度强化学习的QoS感知数据中心网络重构方法

在动态和非均匀网络流量下保持高性能运行已成为当前数据中心网络（DCN）的一项技术挑战。为了为各种应用提供更好的服务质量（QoS），这项工作提出了一种基于深度强化学习（DCR2L）的动态自适应DCN重新配置框架。所提出的框架已集成到DCN的SDN控制平面中，实现了实时和自动的DCN重新配置。DCR2L框架的性能在我们的DCN实验室中通过实验证明，包括4个机架和16个服务器。实验结果表明，在平均网络带宽为2.3 Gb / s的情况下，基于DCR2L的网络延迟提高了6.9％。根据实验中测得的流量和物理参数，在电气和光学DCN以及不同数据中心规模的情况下，通过各种QoS要求的实际流量对DCR2L框架的性能进行了数值评估。在OMNeT ++仿真器中建立了叶脊椎电气网络和OPSquare光网络。对于由16个机架和320台服务器组成的数据平面网络，结果表明，与传统启发式方法相比，DCR2L框架在总叶林下将叶-脊柱下的网络延迟提高了16.4％，将OPSquare下的网络延迟提高了24.6％ 。对于10,240台服务器的DCN规模，DCR2L框架为叶-脊椎电网提供了多达12.5％的延迟降低，为OPSquare光学网络提供了高达17.8％的延迟改进。