QoS-aware data center network reconfiguration method based on deep reinforcement learning

Abstract

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.