DPU empowered intelligent congestion control mechanism for the intelligent computing center network - CHEN Jinqian; GUO Shaoyong; LIU Chang; QI Feng; QIU Xuesong (Tongxin xuebao)
Addressing the issue of frequent network congestion due to high-frequency interactions between intelligent computing center clusters, which compromised the real-time performance of intelligent services, a congestion control model driven by deep reinforcement learning algorithm was constructed with the data processing unit (DPU). By integrating pruning and quantization, the model was lightweighted. Moreover, the model was transformed into the efficient gradient-boosted decision tree through knowledge distillation method, allowing for precise matching of control actions with real-time network conditions. Simulation results show that the proposed mechanism is demonstrated to outperform existing methods in terms of generalization capability and control effectiveness. The network’s effective throughput and fairness index JAIN are increased by more than 10.8% and 8.9%, respectively, across various experimental scenarios. P99 end-to-end latency and packet loss rate are reduced by more than 17.31% and 11.47%, respectively. The completion time of data flow transfer tasks in parallel computing scenarios is decreased by more than 11.23%. Additionally, rapid response capabilities to sudden changes in network status are exhibited.