Adaptive-BBR: Fine-Grained Congestion Control with Improved Fairness and Low Latency
Ming Yang, Peng Yang, Chaozhun Wen, Qiong Liu, Jingjing Luo, Li Chen Yu · 2019
Traditional loss-based congestion control protocols interpret packet loss as network congestion. Recently, Google proposed BBR, which is a congestion-based congestion control protocol. It employs delivery rate as the knob for congestion control, which achieves higher throughput and lower latency. Interestingly, BBR is found to have a preference for longer round-trip time (RTT) flows, which enjoy higher bandwidth ratio compared to flows with shorter RTT. To address this fairness issue, we proposed Adaptive-BBR, which creatively uses adaptive pacing gain to adjust the sending rate. The objective is that, via the proposed fine-grained adaptive mechanism, flows with different RTTs share similar portion of bottleneck bandwidth. Simulation results show that Adaptive-BBR can improve fairness by at least 47.8 %, and reduce average queuing delay by up to 93.3%, compared with that of BBR.