An Analysis of the Disturbance on TCP Network Congestion

Abstract

In this study, the disturbance and uncertainty on nonlinear and time varying systems as Active Queue Management (AQM) is analyzed. Many of AQM schemes have been proposed to regulate a queue size close to a reference level with the least variance. We apply a normal range of disturbances and uncertainty such as variable user numbers, variable link capacity, noise, and unresponsive flows, to the three AQM methods: Random Early Detection (RED), Proportional-Integral (PI) and Improved Neural Network (INN) AQM. Then we examine some important factors for TCP network congestion control such as queue size, drop probability, variance and throughput in NS-2 simulator, and then compare three AQM algorithms with these factors on congestion conditions. We present the performance of the INN controller in desired queue tracking and disturbance rejection is high.