The case for model-driven interpretability of delay-based congestion control protocols

Muhammad Khan, Yasir Zaki, Shiva Iyer, Talal Ahamd, Thomas Poetsch, Jay Chen, Anirudh Sivaraman, Lakshmi Subramanian

Research output: Contribution to journalArticlepeer-review


Analyzing and interpreting the exact behavior of new delay-based congestion control protocols with complex non-linear control loops is exceptionally difficult in highly variable networks such as cellular networks. This paper proposes a Model-Driven Interpretability (MDI) congestion control framework, which derives a model version of a delay-based protocol by simplifying a congestion control protocol’s response into a guided random walk over a two-dimensional Markov model. We demonstrate the case for the MDI framework by using MDI to analyze and interpret the behavior of two delay-based protocols over cellular channels: Verus and Copa. Our results show a successful approximation of throughput and delay characteristics of the protocols’ model versions across variable network conditions. The learned model of a protocol provides key insights into an algorithm’s convergence properties.

Original languageEnglish (US)
Pages (from-to)19-25
Number of pages7
JournalComputer Communication Review
Issue number1
StatePublished - Jan 31 2021


  • Congestion Control
  • Markov Model

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications


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