  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.
