Combining reinforcement learning method to enhance LEDBAT++ over diversified network environments

Abstract

LEDBAT++ is a novel less-than-best-effort congestion control algorithm. However, it still shows aggressiveness when competing with CUBIC in shallow buffer networks or with BBRv2 in low latency networks. In order to maintain its low-priority performance over diversified network environments, we propose PeaceKeeper, which combines reinforcement learning algorithm to dynamically adjust the target based on the network state. Extensive simulations show that, compared to LEDBAT++, the throughput of the primary flow competing with PeaceKeeper improved by 30.76% to 173.63%. Additionally, compared to heuristics adjusting the target, PeaceKeeper increases the link bandwidth utilization by 50.91%.