PCC Vivace: Online-Learning Congestion Control
PCC Vivace: Online-Learning Congestion Control
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发表时间：
2018
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通讯作者：
Mo Dong;Tong Meng;Doron Zarchy;Engin Arslan;Y. Gilad;Brighten Godfrey;Michael Schapira
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作者：
Mo Dong;Tong Meng;Doron Zarchy;Engin Arslan;Y. Gilad;Brighten Godfrey;Michael Schapira
TCP的拥塞控制体系结构的性能是出了名的差。因此，近年来，学术界和工业界对拥塞控制的新方法的兴趣激增。然而，我们发现，过去的方法达不到理想的性能。我们利用丰富的在线（凸）优化机器学习的文献中的思想来设计Vivace，一种新的速率控制协议，设计在最近提出的PCC框架。我们的理论和实验分析表明，Vivace在性能（吞吐量，延迟，丢失），收敛速度，缓解缓冲区膨胀，对不断变化的网络条件的反应能力以及对传统TCP的友好性方面明显优于传统TCP变体，PCC框架的先前实现和BBR。Vivace只需要发送端的更改，因此易于部署。
TCP’s congestion control architecture suffers from notoriously bad performance. Consequently, recent years have witnessed a surge of interest in both academia and industry in novel approaches to congestion control. We show, however, that past approaches fall short of attaining ideal performance. We leverage ideas from the rich literature on online (convex) optimization in machine learning to design Vivace, a novel rate-control protocol, designed within the recently proposed PCC framework. Our theoretical and experimental analyses establish that Vivace significantly outperforms traditional TCP variants, the previous realization of the PCC framework, and BBR in terms of performance (throughput, latency, loss), convergence speed, alleviating bufferbloat, reactivity to changing network conditions, and friendliness towards legacy TCP in a range of scenarios. Vivace requires only sender-side changes and is thus readily deployable.