Performance Analysis of Active Queue Management Scheme for Bursty and Correlated Multi-Class Traffic

With the rapid growth of Internet traffic, the control of congestion to enable different types of Internet traffic to satisfy specified Quality of Service (QoS) constraints is becoming increasingly important. This motivates the stochastic analysis of a discrete-time queueing system for the performance evaluation of active queue management (AQM) scheme 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. The stochastic analysis of the queue considered could be of interest for the performance evaluation of AQM for the multiple-class traffic with short range dependent (SRD) traffic characteristics. Analytical expressions for various performance metrics are computed and typical numerical experiments are included to illustrate the credibility of the proposed mechanism in the context of external bursty and correlated traffic. These experiments clearly demonstrate how different threshold settings can provide different tradeoffs between loss probability and delay to suit different service requirements. The effect of input parameters on various performance metrics, burstiness and correlations for the arrival process are also presented. The model is applicable to high speed networks which use slotted protocols.