Lightweight Automatic ECN Tuning Based on Deep Reinforcement Learning With Ultra-Low Overhead in Datacenter Networks

Abstract

In modern datacenter networks (DCNs), mainstream congestion control (CC) mechanisms essentially rely on Explicit Congestion Notification (ECN) to reflect congestion. The traditional static ECN threshold performs poorly under dynamic scenarios, and setting a proper ECN threshold under various traffic patterns is challenging and time-consuming. The recently proposed reinforcement learning (RL) based ECN Tuning algorithm (ACC) consumes a large number of computational resources, making it difficult to deploy on switches. In this paper, we present a lightweight and hierarchical automated ECN tuning algorithm called LAECN, which can fully exploit the performance benefits of deep reinforcement learning with ultra-low overhead. The simulation results show that LAECN improves performance significantly by reducing latency and increasing throughput in stable network conditions, and also shows consistent high performance in small flows network environments. For example, LAECN effectively improves throughput by up to 47%, 34%, 32% and 24% over DCQCN, TIMELY, HPCC and ACC, respectively.