BBQ: Dynamic-Buffer-Driven Automatic ECN Tunning in Datacenter

Yucheng Zhang, Yu Fan, Yao Xiao, Haoyang Chen, Pengjin Xie, Liang Liu, Huadong Ma

Abstract: The current deployment of extremely shallow-shared-buffer switches in data center networks has posed challenges to widely adopted ECN-based congestion control algorithms, leading to the issue of ECN failure. Switches may not allocate sufficient buffer space for each port, leading to the possibility that the ECN marking threshold exceeds the buffer limit per port. This results in excessive packet loss during bursts, even before the ECN markings take effect. To address this problem, we propose BBQ, an automatic ECN tuning system based on reinforcement learning. BBQ ensures that the ECN threshold does not exceed the buffer capacity allocated to the port, thus avoiding the ECN failure issue. Besides, BBQ is designed to adapt to switches with varying buffer sizes ensuring generalization. We validate the effectiveness of BBQ through experiments conducted with shallow buffering and high bursts. The results show that BBQ efficiently controls the packet loss rate of incast flows to within 3%, 1.2 times lower than State-of-the-Arts in shallow-buffered environments.