Adaptive Dynamic Congestion Avoidance with Master Equation

TL;DR

This paper introduces an adaptive RED algorithm using a Master Equation approach, eliminating the need for a free parameter and dynamically computing queue probabilities for improved congestion control.

Contribution

It presents a novel adaptive RED variant based on a Markov process that removes the free parameter and automatically computes transition probabilities.

Findings

Enhanced congestion avoidance performance in simulations

Elimination of the queue weight parameter

Automatic computation of transition rates and probabilities

Abstract

This paper proposes an adaptive variant of Random Early Detection (RED) gateway queue management for packet-switched networks via a discrete state analog of the non-stationary Master Equation i.e. Markov process. The computation of average queue size, which appeared in the original RED algorithm, is altered by introducing a probability $P(l,t)$, which defines the probability of having $l$ number of packets in the queue at the given time $t$, and depends upon the previous state of the queue. This brings the advantage of eliminating a free parameter: queue weight, completely. Computation of transition rates and probabilities are carried out on the fly, and determined by the algorithm automatically. Simulations with unstructured packets illustrate the method, the performance of the adaptive variant of RED algorithm, and the comparison with the standard RED.