MPCC: Online learning multipath transport

Tomer Gilad, Neta Rozen-Schiff, P. Brighten Godfrey, Costin Raiciu, Michael Schapira

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

22 Scopus citations

Abstract

Multipath transport, as embodied in MPTCP, is deployed to improve throughput and reliability in mobile and residential access networks, with additional use-cases including spreading load in data centers and WANs. However, MPTCP is fundamentally tied to TCP Reno's legacy AIMD algorithm, and significantly lags behind the performance of modern single-path designs. Consequently, MPTCP fails to achieve high performance in many real-world environments. We present MPCC, a high-performance multipath congestion control architecture. To achieve our combined goals of fairness and high performance in challenging environments, MPCC employs online convex optimization (a.k.a. online learning). In experiments with a kernel implementation on emulated and live networks, MPCC significantly outperforms MPTCP.

Original languageAmerican English
Title of host publicationCoNEXT 2020 - Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies
PublisherAssociation for Computing Machinery, Inc
Pages121-135
Number of pages15
ISBN (Electronic)9781450379489
DOIs
StatePublished - 23 Nov 2020
Event16th ACM Conference on Emerging Networking Experiment and Technologies, CoNEXT 2020 - Barcelona, Spain
Duration: 1 Dec 20204 Dec 2020

Publication series

NameCoNEXT 2020 - Proceedings of the 16th International Conference on Emerging Networking EXperiments and Technologies

Conference

Conference16th ACM Conference on Emerging Networking Experiment and Technologies, CoNEXT 2020
Country/TerritorySpain
CityBarcelona
Period1/12/204/12/20

Bibliographical note

Publisher Copyright:
© 2020 ACM.

Keywords

  • congestion control
  • multipath

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