Power-Aware Traffic Engineering for Data Center Networks via Deep Reinforcement Learning

The issue of high energy consumption and low energy utilization in data center networks (DCNs) has always been the focus of attention of both academia and industry. One general solution is to select a subset of network devices that can meet the traffic transmission requirements, thereby turning off the remaining redundant devices. However, modeling the problem as integer linear programming introduces significant time overhead, while heuristic approaches often suffer from poor generalizability. In this paper, we propose GreenDCN.ai, a closed-loop control system, which utilizes In-band Network Telemetry to collect the network-wide device-internal state, and leverages a Deep Reinforcement Learning-based energy-saving algorithm to make rapid decisions to turn on or off network device ports in response to the real-time network state. The trained GreenDCN.ai can adaptively adjust its energy-saving strategy without human intervention when the DCN topology changes. Besides, based on the regularity of the DCN topology, we design two training complexity reduction methods to address the non-convergence issue under large-scale DCN topologies. Specifically, we split the large-scale DCN topology into sub-topologies for parallel training on each sub-topology without breaking the DCN topology connectivity. Evaluation on software P4 switches suggests that GreenDCN.ai can achieve stable convergence within 590 episodes, generate effective action decisions within 79 mu s, and save about 34% to 39% of the network energy consumption.