Improvement of BBRv2 Congestion Control Algorithm Based on Flow-aware ECN

Abstract

Google proposed a new congestion control algorithm (CCA) based on bottleneck bandwidth and round-trip propagation time (BBR), which is considered to open a new era of congestion control. BBR creates a network path model by measuring the available bottleneck bandwidth and the minimum round-trip time (RTT) to maximize delivery rate and minimize latency. The BBR v2 algorithm is a recently updated version by Google, which aims to improve some of the problems in the original BBR (BBRv1) algorithm, such as interprotocol fairness issues, RTT fairness issues, and excessive retransmissions. The BBRv2 evaluation results show that it can improve the coexistence with the loss_based algorithm and alleviate some of the shortcomings in BBRv1. However, when multiple BBRv2 flows enter the same link at different times, fair convergence cannot be achieved, and RTT fairness still exists. Based on these problems, we analyze the root cause and proposed an improved algorithm BBRv2+, which uses flow-aware explicit congestion notification (ECN) to quantify queue information and feedback on the accurate congestion degree. BBRv2+ algorithm can avoid blind window constraints and selectively mark packets so that different flows can converge to fairness. In the simulation experiment of Network Simulator 3 (NS3), the results show that the BBRv2+ algorithm can improve intraprotocol fairness and RTT fairness and ensure bandwidth utilization and interprotocol fairness.