Plume: Lightweight and generalized congestion control with deep reinforcement learning

Abstract

Congestion control (CC) is always an important issue in the field of networking, and the enthusiasm for its research has never diminished in both academia and industry. In current years, due to the rapid development of machine learning (ML), the combination of reinforcement learning (RL) and CC has a striking effect. However, These complicated schemes lack generalization and are too heavyweight in storage and computing to be directly implemented in mobile devices. In order to address these problems, we propose Plume, a high-performance, lightweight and generalized RL-CC scheme. Plume proposes a lightweight framework to reduce the overheads while preserving the original performance. Besides, Plume innovatively modifies the framework parameters of the reward function during the retraining process, so that the algorithm can be applied to a variety of scenarios. Evaluation results show that Plume can retain almost all the performance of the original model but the size and decision latency can be reduced by more than 50% and 20%, respectively. Moreover, Plume has better performances in some special scenes.