基于场景变化的传输控制协议拥塞控制切换方案  

Transmission control protocol congestion control switching scheme based on scenario change

作　　者：赖涵光 李清 江勇 LAI Hanguang;LI Qing;JIANG Yong(Shenzhen International Graduate School,Tsinghua University,Shenzhen Guangdong 518055,China;Institute of Future Networks,Southern University of Science and Technology,Shenzhen Guangdong 518055,China)

机构地区：[1]清华大学深圳国际研究生院,广东深圳518055 [2]南方科技大学未来网络研究院,广东深圳518055

出　　处：《计算机应用》2022年第4期1225-1234,共10页journal of Computer Applications

基　　金：广东省重点领域研发计划项目(2018B010113001)。

摘　　要：针对轻量级基于学习的拥塞控制算法在某些场景下性能表现会出现断崖式下滑的问题,提出了一种基于场景变化的传输控制协议拥塞控制切换方案。首先,该方案模拟实时的网络环境;然后,根据实时的环境参数来识别场景;最后,将当前的拥塞控制算法切换至该场景下相对最优的轻量级基于学习的拥塞控制算法。实验结果表明,所提方案相较于原来使用单个拥塞控制算法的方案,例如测量瓶颈链路带宽和时延的拥塞控制(BBR)方案、面向性能的拥塞控制(PCC)方案等,可以使不同场景下的网络性能得到显著提升,总吞吐量增幅达到5%以上,总时延降幅达到10%以上。Aiming at the problem that the performance of lightweight learning-based congestion control algorithms will fall off a cliff in some scenarios,a transmission control protocol congestion control switching scheme based on scenario change was proposed.Firstly,the real-time network environment was simulated by this scheme.Then,the scenario was identified according to the real-time environment parameters.Finally,the current congestion control algorithm was switched to the relatively optimal lightweight learning-based congestion control algorithm in this scenario.Experimental results prove that the proposed scheme is able to significantly improve network performance compared to the original schemes using a single congestion control algorithm,such as congestion control based on measuring Bottleneck Bandwidth and Round-trip propagation time(BBR)and Performance-oriented Congestion Control(PCC)with a total throughput increase of more than5%and a total delay drop of more than 10%.

关 键 词：拥塞控制 场景变化 轻量级 基于学习 传输控制协议 

分 类 号：TP393.0[自动化与计算机技术—计算机应用技术]