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The Great Internet TCP Congestion Control Census

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Published:08 June 2020Publication History

ABSTRACT

In 2016, Google proposed and deployed a new TCP variant called BBR. BBR represents a major departure from traditional congestion control as it uses estimates of bandwidth and round-trip delays to regulate its sending rate. BBR has since been introduced in the upstream Linux kernel and deployed by Google across its data centers. Since the last major study to identify TCP congestion control variants on the Internet was done before BBR, it is timely to conduct a new census to give us a sense of the current distribution of congestion control variants on the Internet. To this end, we designed and implemented Gordon, a tool that allows us to measure the congestion window (cwnd) corresponding to each successive RTT in the TCP connection response of a congestion control algorithm. To compare a measured flow to the known variants, we created a localized bottleneck and introduced a variety of network changes like loss events, changes in bandwidth and delay, while normalizing all measurements by RTT. We built an offline classifier to identify the TCP variant based on the cwnd trace over time.

Our results suggest that CUBIC is currently the dominant TCP variant on the Internet, and is deployed on about 36% of the websites in the Alexa Top 20,000 list. While BBR and its variant BBR G1.1 are currently in second place with a 22% share by website count, their present share of total Internet traffic volume is estimated to be larger than 40%. We also found that Akamai has deployed a unique loss-agnostic rate-based TCP variant on some 6% of the Alexa Top 20,000 websites and there are likely other undocumented variants. Therefore, the traditional assumption that TCP variants ''in the wild'' will come from a small known set is not likely to be true anymore. Our results suggest that some variant of BBR seems poised to replace CUBIC as the next dominant TCP variant on the Internet.

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References

  1. Neal Cardwell. 2017. tcp_bbr: add BBR congestion control. (2017). https://bit.ly/2VAJcDDGoogle ScholarGoogle Scholar
  2. Neal Cardwell, Yuchung Cheng, C. Stephen Gunn, Soheil Hassas Yeganeh, and Van Jacobson. 2017a. BBR: Congestion-based Congestion Control. CACM, Vol. 60, 2 (2017), 58--66.Google ScholarGoogle Scholar
  3. Alexa Internet Inc. 2018. The Top 500 websites on the Internet. (2018). https://www.alexa.com/topsitesGoogle ScholarGoogle Scholar
  4. Peng Yang, Juan Shao, Wen Luo, Lisong Xu, Jitendra Deogun, and Ying Lu. 2011. TCP Congestion Avoidance Algorithm Identification. IEEE/ACM Transactions on Networking, Vol. 22, 4 (2011), 1311--1324.Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Peng Yang and Lisong Xu. 2011. A survey of deployment information of delay-based TCP congestion avoidance algorithm for transmitting multimedia data. In Proceedings of GLOBECOM Workshops.Google ScholarGoogle ScholarCross RefCross Ref

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  1. The Great Internet TCP Congestion Control Census

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          cover image ACM Conferences
          SIGMETRICS '20: Abstracts of the 2020 SIGMETRICS/Performance Joint International Conference on Measurement and Modeling of Computer Systems
          June 2020
          124 pages
          ISBN:9781450379854
          DOI:10.1145/3393691

          Copyright © 2020 Owner/Author

          Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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          Association for Computing Machinery

          New York, NY, United States

          Publication History

          • Published: 8 June 2020

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