Model-driven Delay-based Congestion Control for Cellular Networks

Delay-based congestion control protocols appear to respond more efficiently to the unique properties of cellular networks compared to loss-based protocols. Unfortunately, delay-based congestion control protocols are often hard to understand and interpret by protocol designers due to their non-linear nature. As a result, existing models tend to include many oversimplifications and assumptions that limit their applicability to real cellular networks. In this paper, we develop a new stochastic two-dimensional discrete-time Markov modeling approach that dramatically simplifies the understanding of delay-based congestion control protocols. This model allows us to analyze a protocol’s behavior and to replicate its analysis under different network scenarios. We use the Verus congestion control protocol as a case study to demonstrate that the model’s performance matches that of Verus. The modeling approach developed in this paper is extensible to a variety of different settings and protocols. We describe the central elements that a designer should specify to achieve comparable performance with other delay-based congestion control protocols.