2021
DOI: 10.1145/3457175.3457179
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The case for model-driven interpretability of delay-based congestion control protocols

Abstract: 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 fr… Show more

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Cited by 5 publications
(2 citation statements)
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“…However, this can be an exhaustive and time-consuming task, which is out of scope for this paper. Thus, to be able to quickly provide a proof-of-concept implementation of one of the new cellular CC protocols into the ALCC Android library, we relied on using the Model-Driven Interpretable (MDI) congestion control [29] approach. MDI allows approximating any congestion control algorithm as a general discrete-time Markov model by a 2-dimensional state space, represented in the form of a state-transition probability matrix for that algorithm.…”
Section: Implementing Alcc On a Mobile Devicementioning
confidence: 99%
See 1 more Smart Citation
“…However, this can be an exhaustive and time-consuming task, which is out of scope for this paper. Thus, to be able to quickly provide a proof-of-concept implementation of one of the new cellular CC protocols into the ALCC Android library, we relied on using the Model-Driven Interpretable (MDI) congestion control [29] approach. MDI allows approximating any congestion control algorithm as a general discrete-time Markov model by a 2-dimensional state space, represented in the form of a state-transition probability matrix for that algorithm.…”
Section: Implementing Alcc On a Mobile Devicementioning
confidence: 99%
“…The App relies on MDI as the main congestion control logic. We have utilized a transition probability matrix of Verus that was trained over 1000 cellular traces as described in [29]. Figure 4 shows the throughput and delay performances of the Android ALCC app(using Verus MDI) compared to the TCP cubic performance measured using an upload of the same file with scp 3 from a laptop that was tethered to the Android phone.…”
Section: Implementing Alcc On a Mobile Devicementioning
confidence: 99%