Generalized TCP-RED dynamical model for Internet congestion control

TL;DR

This paper develops and analyzes a nonlinear dynamical model for Internet congestion control, introducing new control parameters to enhance stability and robustness against chaos, with potential for improved adaptive algorithms.

Contribution

It generalizes existing models by adding control parameters via packet drop probability, enabling better stability analysis and practical stability domain determination.

Findings

The model exhibits low-dimensional chaos under certain conditions.

New control parameters extend the stability domain.

Numerical simulations confirm improved stability with the new parameters.

Abstract

Adaptive management of traffic congestion in the Internet is a complex problem that can gain useful insights from a dynamical approach. In this paper we propose and analyze a one-dimensional, discrete-time nonlinear model for Internet congestion control at the routers. Specifically, the states correspond to the average queue sizes of the incoming data packets and the dynamical core consists of a monotone or unimodal mapping with a unique fixed point. This model generalizes a previous one in that additional control parameters are introduced via the data packet drop probability with the objective of enhancing stability. To make the analysis more challenging, the original model was shown to exhibit the usual features of low-dimensional chaos with respect to several system and control parameters, e.g., positive Lyapunov exponents and Feigenbaum-like bifurcation diagrams. We concentrate first on the theoretical aspects that may promote the unique stationary state of the system to a global attractor, which in our case amounts to global stability. In a second step, those theoretical results are translated into stability domains for robust setting of the new control parameters in practical applications. Numerical simulations confirm that the new parameters make it possible to extend the stability domains, in comparison with previous results. Therefore, the present work may lead to an adaptive congestion control algorithm with a more stable performance than other algorithms currently in use.