Plume:Lightweight and Generalized Congestion Control with Deep Reinforcement Learning  

作　　者：Dehui Wei Jiao Zhang Xuan Zhang Chengyuan Huang 

机构地区：[1]State Key Laboratory of Networking and Switching Technology,BUPT 100876,China

出　　处：《China Communications》2022年第12期101-117,共17页中国通信（英文版）

基　　金：supported by National Natural Science Foundation of China (NSFC) under Grant (No.61872401);National Natural Science Foundation of China (NSFC) under Grant (No.62132022);Fok Ying Tung Education Foundation (No.171059);BUPT Excellent Ph.D.Students Foundation (No. CX2021102)

摘　　要：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.

关 键 词：congestion control deep reinforcement learning LIGHTWEIGHT GENERALIZATION 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP393.06[自动化与计算机技术—控制科学与工程]