Modeling discrete-time analytical models based on random early detection : exponential and linear

Abdel-Jaber, Hussein
Thabtah, Fadi
Woodward, Mike
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World Scientific Publishing Co. Pte Ltd
Congestion control is among primary topics in computer network in which random early detection (RED) method is one of its common techniques. Nevertheless, RED suffers from drawbacks in particular when its "average queue length" is set below the buffer's "minimum threshold" position which makes the router buffer quickly overflow. To deal with this issue, this paper proposes two discrete-time queue analytical models that aim to utilize an instant queue length parameter as a congestion measure. This assigns mean queue length (mql) and average queueing delay smaller values than those for RED and eventually reduces buffers overflow. A comparison between RED and the proposed analytical models was conducted to identify the model that offers better performance. The proposed models outperform the classic RED in regards to mql and average queueing delay measures when congestion exists. This work also compares one of the proposed models (RED-Linear) with another analytical model named threshold-based linear reduction of arrival rate (TLRAR). The results of the mql, average queueing delay and the probability of packet loss for TLRAR are deteriorated when heavy congestion occurs, whereas, the results of our RED-Linear were not impacted and this shows superiority of our model. © 2015 World Scientific Publishing Company.
This article is not available at CUD collection. The version of scholarly record of this Article is published in International Journal of Modeling, Simulation, and Scientific Computing (2015), available online at:
Congestion control (communication), Queueing networks, Queueing theory, Average queue lengths, Better performance, Congestion measure, Discrete time queues, Linear reduction, Mean queue lengths, Queueing delays, Random Early Detections, Analytical models
Abdel-Jaber, H., Thabtah, F., & Woodward, M. (2015). Modeling discrete-time analytical models based on random early detection: Exponential and linear. International Journal of Modeling, Simulation, and Scientific Computing, 6(3).