The coflow scheduling in data-parallel clusters can improve application-level communication performance. The existing coflow scheduling method without prior knowledge usually uses multi-level feedback queue (MLFQ) with fixed threshold parameters, which is insensitive to coflow traffic characteristics. Manual adjustment of the threshold parameters for different application scenarios often has long optimization period and is coarse in optimization granularity. We propose M-DRL, a deep reinforcement learning based coflow traffic scheduler by dynamically setting thresholds of MLFQ to adapt to the coflow traffic characteristics, and reduces the average coflow completion time. Trace-driven simulations on the public dataset show that coflow communication stages using M-DRL complete 2.08x(6.48x) and 1.36x(1.25x) faster on average coflow completion time (95-th percentile) in comparison to per-flow fairness and Aalo, and is comparable to SEBF with prior knowledge.

数据并行集群中的同流调度可以提高应用程序级别的通信性能。现有的没有先验知识的同流调度方法通常使用具有固定阈值参数的多级反馈队列（MLFQ），这对同流流量特性不敏感。对于不同的应用场景，手动调整阈值参数通常具有较长的优化周期，并且优化粒度较粗。我们通过动态设置MLFQ阈值以适应同流交通特征，并减少平均同流完成时间，提出了M-DRL，这是一种基于深度强化学习的同流交通调度程序。在公共数据集上的跟踪驱动模拟显示，使用M-DRL的同流通信阶段完成了2.08x（6.48x）和1.36x（1）。