Computers Can Learn from the Heuristic Designs and Master Internet Congestion Control

Chen Yu Yen, Soheil Abbasloo, H. Jonathan Chao

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this work, for the first time, we demonstrate that computers can automatically learn from observing the heuristic efforts of the last four decades, stand on the shoulders of the existing Internet congestion control (CC) schemes, and discover a better-performing one. To that end, we address many different practical challenges, from how to generalize representation of various existing CC schemes to serious challenges regarding learning from a vast pool of policies in the complex CC domain and introduce Sage. Sage is the first purely data-driven Internet CC design that learns a better scheme by harnessing the existing solutions. We compare Sage's performance with the state-of-the-art CC schemes through extensive evaluations on the Internet and in controlled environments. The results suggests that Sage has learned a better-performing policy. While there are still many unanswered questions, we hope our data-driven framework can pave the way for a more sustainable design strategy.

Original languageEnglish (US)
Title of host publicationSIGCOMM 2023 - Proceedings of the ACM SIGCOMM 2023 Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages20
ISBN (Electronic)9798400702365
StatePublished - Sep 10 2023
EventACM SIGCOMM 2023 Conference - New York, United States
Duration: Sep 10 2023Sep 14 2023

Publication series

NameSIGCOMM 2023 - Proceedings of the ACM SIGCOMM 2023 Conference


ConferenceACM SIGCOMM 2023 Conference
Country/TerritoryUnited States
CityNew York


  • TCP
  • congestion control
  • data-driven reinforcement learning

ASJC Scopus subject areas

  • Hardware and Architecture
  • Software
  • Computer Networks and Communications


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