Discrete-time performance analysis of a congestion control mechanism based on RED under multi-class bursty and correlated traffic

Internet traffic congestion control using queue thresholds is a well known and effective mechanism. This motivates the stochastic analysis of a discrete-time queueing systems for the performance evaluation of the active queue management (AQM) based congestion control mechanism called Random Early Detection (RED) with bursty and correlated traffic using a two-state Markov-Modulated Bernoulli arrival process (MMBP-2) as the traffic source. A two-dimensional discrete-time Markov chain is introduced to model the RED mechanism for two traffic classes where each dimension corresponds to a traffic class with its own parameters. This mechanism takes into account the reduction of incoming traffic arrival rate due to packets dropped probabilistically with the drop probability increasing linearly with system contents. The stochastic analysis of the queue considered could be of interest for the performance evaluation of the RED mechanism for the multi-class traffic with short range dependent (SRD) traffic characteristics. The performance metrics including mean system occupancy, mean packet delay, packet loss probability and system throughput are computed from the analytical model for a dropping policy which is a function of the thresholds and maximum drop probability. Typical numerical results are included to illustrate the credibility of the proposed mechanism in the context of external bursty and correlated traffic. These results clearly demonstrate how different threshold settings can provide different trade-offs between loss probability and delay to suit different service requirements. The effects on various performance measures of changes in the input parameters and of burstiness and correlations exhibited by the arrival process are also presented. The model would be applicable to high-speed networks which use slotted protocols. Crown Copyright 2007 Published by Elsevier Inc. All rights reserved.