  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.
