Explicit Congestion Notification (ECN)-based congestion control schemes have been widely adopted in high-speed data center networks (DCNs), where the ECN marking threshold plays a determinant role in guaranteeing a packet lossless DCN. However, existing approaches either employ static settings with immutable thresholds that cannot be dynamically self-adjusted to adapt to network dynamics, or fail to take into account many-to-one traffic patterns and different requirements of different types of traffic, resulting in relatively poor performance. To address these problems, this paper proposes a novel learning-based automatic ECN tuning scheme, named PET, based on the multi-agent Independent Proximal Policy Optimization (IPPO) algorithm. PET dynamically adjusts ECN thresholds by fully considering pivotal congestion-contributing factors, including queue length, output data rate, output rate of ECN-marked packets, current ECN threshold, the extent of incast, and the ratio of mice and elephant flows. PET adopts the Decentralized Training and Decentralized Execution (DTDE) paradigm and combines offline and online training to accommodate network dynamics. PET is also fair and readily deployable with commodity hardware. Comprehensive experimental results demonstrate that, compared with state-of-the-art static schemes and the learning-based automatic scheme, our PET achieves better performance in terms of flow completion time, convergence rate, queue length variance, and system robustness.

翻译：基于显式拥塞通知（ECN）的拥塞控制方案已被广泛应用于高速数据中心网络（DCN），其中ECN标记阈值在保证无损DCN中起着决定性作用。然而，现有方法要么采用不可变阈值的静态设置，无法动态自适应网络动态变化，要么未能考虑多对一流量模式及不同类型流量的差异化需求，导致性能相对较差。为解决上述问题，本文提出一种新颖的基于学习的自动ECN调优方案PET，该方案基于多智能体独立近端策略优化（IPPO）算法。PET通过全面考虑关键拥塞贡献因素（包括队列长度、输出数据速率、ECN标记数据包输出速率、当前ECN阈值、多对一流量聚合程度以及老鼠流与大象流比例）动态调整ECN阈值。PET采用分散式训练与分散式执行（DTDE）范式，并结合离线和在线训练以适应网络动态变化。PET同时具备公平性且易于在商用硬件上部署。综合实验结果表明，与现有最先进的静态方案及基于学习的自动方案相比，我们的PET在流完成时间、收敛速度、队列长度方差及系统鲁棒性方面均取得了更优性能。