pantheon the training ground for internet congestion
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Pantheon: the training ground for Internet congestion-control research https://pantheon.stanford.edu Francis Y. Yan , Jestin Ma , Greg D. Hill , Deepti Raghavan , Riad S. Wahby , Philip Levis , Keith Winstein Stanford


  1. Pantheon: the training ground for Internet congestion-control research https://pantheon.stanford.edu Francis Y. Yan † , Jestin Ma † , Greg D. Hill † , Deepti Raghavan ¶ , Riad S. Wahby † , Philip Levis † , Keith Winstein † † Stanford University, ¶ Massachusetts Institute of Technology July 13, 2018 Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 1 / 42

  2. Introduction Congestion control Cornerstone problem in computer networking Avoids congestion collapse Allocates resources among users Affects every application using TCP socket Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 2 / 42

  3. Introduction Status quo of congestion control research Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 3 / 42

  4. Introduction Status quo of congestion control research Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 3 / 42

  5. Introduction Status quo of congestion control research Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 3 / 42

  6. Introduction Inconsistent behaviors Better Figure: Colombia to AWS Brazil (cellular, 1 flow, 3 trials, P1391) Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 4 / 42

  7. Introduction Inconsistent behaviors Better Figure: Colombia to AWS Brazil (cellular, 1 flow, 3 trials, P1391) Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 4 / 42

  8. Introduction Challenges and problems Every emerging algorithm claims to be the “state-of-the-art” ... compared with other algorithms that they picked ... evaluated on their own testbeds in real world ... and/or on simulators/emulators with their settings ... based on the specific results that they collected Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 5 / 42

  9. Introduction Challenges and problems ... compared with other algorithms that they picked ... evaluated on their own testbeds in real world ... and/or on simulators/emulators with their settings ... based on the specific results that they collected Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 6 / 42

  10. Introduction Challenges and problems ... compared with other algorithms that they picked = ⇒ must acquire, compile, and execute prior algorithms ... evaluated on their own testbeds in real world ... and/or on simulators/emulators with their settings ... based on the specific results that they collected Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 6 / 42

  11. Introduction Challenges and problems ... compared with other algorithms that they picked = ⇒ must acquire, compile, and execute prior algorithms ... evaluated on their own testbeds in real world = ⇒ large service operators: risky to deploy, long turnaround time ... and/or on simulators/emulators with their settings ... based on the specific results that they collected Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 6 / 42

  12. Introduction Challenges and problems ... compared with other algorithms that they picked = ⇒ must acquire, compile, and execute prior algorithms ... evaluated on their own testbeds in real world = ⇒ large service operators: risky to deploy, long turnaround time = ⇒ researchers: on a much smaller scale, results may not generalize ... and/or on simulators/emulators with their settings ... based on the specific results that they collected Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 6 / 42

  13. Introduction Challenges and problems ... compared with other algorithms that they picked = ⇒ must acquire, compile, and execute prior algorithms ... evaluated on their own testbeds in real world = ⇒ large service operators: risky to deploy, long turnaround time = ⇒ researchers: on a much smaller scale, results may not generalize ... and/or on simulators/emulators with their settings = ⇒ how to configure the settings? ... based on the specific results that they collected Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 6 / 42

  14. Introduction Challenges and problems ... compared with other algorithms that they picked = ⇒ must acquire, compile, and execute prior algorithms ... evaluated on their own testbeds in real world = ⇒ large service operators: risky to deploy, long turnaround time = ⇒ researchers: on a much smaller scale, results may not generalize ... and/or on simulators/emulators with their settings = ⇒ how to configure the settings? ... based on the specific results that they collected = ⇒ but the Internet is diverse and evolving Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 6 / 42

  15. Introduction Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 7 / 42

  16. Introduction Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 7 / 42

  17. Introduction Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 7 / 42

  18. Introduction Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 7 / 42

  19. Introduction Shared, reproducible benchmarks can lead to huge leaps performance and transform technologies by making them scientific. Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 8 / 42

  20. Introduction Pantheon: a community evaluation platform for congestion control a common reference set of 15+ benchmark algorithms a diverse testbed of network nodes in 10+ countries Cellular and wired: U.S., Mexico, Brazil, Colombia, India, China Wired networks only: U.K., Australia, Japan, Korea, Saudi Arabia a collection of calibrated emulators and pathological emulators a continous-testing system and a public archive of searchable results at https://pantheon.stanford.edu Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 9 / 42

  21. Introduction This is a reproducible talk! e.g., P123: https://pantheon.stanford.edu/result/123/ Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 10 / 42

  22. Introduction pantheon.stanford.edu Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 11 / 42

  23. Introduction pantheon.stanford.edu Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 11 / 42

  24. Introduction Pantheon: a community resource A common language in congestion control benchmark algorithms shared testbeds public data Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 12 / 42

  25. Introduction Pantheon: a community resource A common language in congestion control benchmark algorithms shared testbeds public data A training ground for congestion control enables faster innovation and more reproducible research e.g., Vivace (NSDI ’18), Copa (NSDI ’18), Indigo: a machine-learned congestion control Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 12 / 42

  26. Pantheon: a community evaluation platform for congestion control Outline Introduction 1 Pantheon: a community evaluation platform for congestion control 2 Calibrated emulators and pathological emulators 3 Ongoing projects 4 Vivace, Copa, and more Indigo Conclusion 5 Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 13 / 42

  27. Pantheon: a community evaluation platform for congestion control A software library of congestion-control algorithms 15+ algorithms TCP Cubic, TCP Vegas, TCP BBR, QUIC Cubic, LEDBAT, WebRTC (media), Sprout, Remy, Verus, PCC, SCReAM, FillP, Vivace, Copa, Indigo, ... Add your own transport protocol (instructions at pantheon.stanford.edu ) Common testing interface A full-throttle flow that runs until killed Measure performance faithfully without modifications Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 14 / 42

  28. Pantheon: a community evaluation platform for congestion control Key findings Measurement study from more than a year of data Performance of congestion-control algorithms varies across the type of network path, path direction, and time Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 15 / 42

  29. Pantheon: a community evaluation platform for congestion control Key finding 1: scheme performance varies by path Better Better Figure: AWS Brazil to Colombia Figure: Stanford to AWS California (cellular, 1 flow, 3 trials, P1392) (cellular, 1 flow, 3 trials, P950) Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 16 / 42

  30. Pantheon: a community evaluation platform for congestion control Key finding 1: scheme performance varies by path Better Better Figure: AWS Brazil to Colombia Figure: AWS Brazil to Colombia (cellular, 1 flow, 3 trials, P1392) (wired, 1 flow, 10 trials, P1271) Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 17 / 42

  31. Pantheon: a community evaluation platform for congestion control Key finding 2: scheme performance varies by path direction Better Better Figure: Colombia to AWS Brazil Figure: AWS Brazil to Colombia (cellular, 1 flow, 3 trials, P1391) (cellular, 1 flow, 3 trials, P1392) Francis Y. Yan (Stanford) Pantheon of Congestion Control July 13, 2018 18 / 42

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