Abstract

Random early detection (RED) is a powerful mechanism used for queue management. Many studies showed that RED has advantages over tail drop (TD). However, most of these studies were based on simulation and does not reflect a conclusive performance evaluation of RED. The need for an analytical evaluation that allows better understanding of RED was addressed by several researchers. The result was several approaches, each partially characterizes RED performance. This work aims to provide a better understanding of the RED algorithm and to quantify the benefits and limitations of using RED queue management by using two analytic models, namely: stochastic-based model; using queuing theory and stochastic modeling and fluid-based model, using stochastic differential equations. The fluid-based model was modified to incorporate smooth nonconforming traffic (e.g. UDP) as well as TCP. The model was verified using simulation results. Our analysis showed that RED outperforms TD most of the time. The stochastic based model showed that RED removes the bias against bursty traffic and helps control the queue size and the delay. On the other hand, it increases the variability of the queue size and hence the jitter. The results showed that RED queue can handle the added UDP traffic while maintaining its normal operation to some extent. However, the increase in the UDP traffic share created oscillation in the queue size and resulted in instability. When the UDP traffic reached 50% of the total capacity the queue starts oscillating wildly which can cause buffer overflow and jitter. The effect of the UDP portion of the overall link capacity on the TCP traffic in the RED queue where studied. It seems that a high UDP rate will not starve the TCP traffic under RED. Another observation was that for higher link capacities the RED performance becomes highly dependent on the sampling rate (alpha). Simulation confirms the above conclusions and matches well with the findings obtained from analysis

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