PCC vivace: Online-learning congestion control

Mo Dong, Tong Meng, Doron Zarchy, Engin Arslan, Yossi Gilad, P. Brighten Godfrey, Michael Schapira

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

258 Scopus citations

Abstract

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.

Original languageAmerican English
Title of host publicationProceedings of the 15th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2018
PublisherUSENIX Association
Pages343-356
Number of pages14
ISBN (Electronic)9781939133014
StatePublished - 2018
Event15th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2018 - Renton, United States
Duration: 9 Apr 201811 Apr 2018

Publication series

NameProceedings of the 15th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2018

Conference

Conference15th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2018
Country/TerritoryUnited States
CityRenton
Period9/04/1811/04/18

Bibliographical note

Publisher Copyright:
© Proceedings of NSDI 2010: 7th USENIX Symposium on Networked Systems Design and Implementation. All rights reserved.

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