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CCAnalyzer: An Efficient and Nearly-Passive Congestion Control Classifier

Authors:

Adithya Abraham Philip,

Nicholas Hungria,

Justine Sherry,

Srinivasan SeshanAuthors Info & Claims

ACM SIGCOMM '24: Proceedings of the ACM SIGCOMM 2024 Conference

Pages 181 - 196

https://doi.org/10.1145/3651890.3672255

Published: 04 August 2024 Publication History

Abstract

We present CCAnalyzer, a novel classifier for deployed Internet congestion control algorithms (CCAs) which is more accurate, more generalizable, and more human-interpretable than prior classifiers. CCAnalyzer requires no knowledge of the underlying CCA algorithms, and it can identify when a CCA is novel - i.e. not in the training set. Furthermore, CCAnalyzer can cluster together servers it believes use the same novel/unknown algorithm. CCAnalyzer correctly identifies all 15 of the default Internet CCAs deployed with Linux, including BBRv1, which no existing classifier can do. Finally, CCAnalyzer can classify server CCAs while being as efficient or better than prior approaches in terms of bytes transferred and runtime. We conduct a measurement study using CCAnalyzer measuring the CCA for 5000+ websites. We find widespread deployment of BBRv1 at large CDNs, and demonstrate how our clustering technique can detect deployments of new algorithms as it discovers BBRv3 although BBRv3 is not in its training set.

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Cited By

Ferreira MWare RKothari YLynce IMartins RNarayan ASherry JVallina-Rodríguez NSuarez-Tángil GLevin DPelsser C(2024)Reverse-Engineering Congestion Control Algorithm BehaviorProceedings of the 2024 ACM on Internet Measurement Conference10.1145/3646547.3688443(401-414)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3646547.3688443

Index Terms

CCAnalyzer: An Efficient and Nearly-Passive Congestion Control Classifier
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Clustering and classification
2. Networks
  1. Network performance evaluation
    1. Network measurement
  2. Network protocols
    1. Transport protocols

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Published In

cover image ACM Conferences

ACM SIGCOMM '24: Proceedings of the ACM SIGCOMM 2024 Conference

August 2024

1033 pages

ISBN:9798400706141

DOI:10.1145/3651890

Co-chairs:
Aruna Seneviratne,
Darryl Veitch,
Program Co-chairs:
Vyas Sekar,
Minlan Yu

This work is licensed under a Creative Commons Attribution-ShareAlike International 4.0 License.

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SIGCOMM: ACM Special Interest Group on Data Communication

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Association for Computing Machinery

New York, NY, United States

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Published: 04 August 2024

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National Science Foundation

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ACM SIGCOMM '24

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SIGCOMM

ACM SIGCOMM '24: ACM SIGCOMM 2024 Conference

August 4 - 8, 2024

NSW, Sydney, Australia

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Overall Acceptance Rate 462 of 3,389 submissions, 14%

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Cited By

Ferreira MWare RKothari YLynce IMartins RNarayan ASherry JVallina-Rodríguez NSuarez-Tángil GLevin DPelsser C(2024)Reverse-Engineering Congestion Control Algorithm BehaviorProceedings of the 2024 ACM on Internet Measurement Conference10.1145/3646547.3688443(401-414)Online publication date: 4-Nov-2024
https://dl.acm.org/doi/10.1145/3646547.3688443

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