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Mathematical Problems in Engineering
Volume 2011, Article ID 872347, 17 pages
Research Article

Self-Tuning Random Early Detection Algorithm to Improve Performance of Network Transmission

Shenzhen City Key Laboratory of Embedded System Design, College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China

Received 22 August 2010; Accepted 26 September 2010

Academic Editor: Ming Li

Copyright © 2011 Jianyong Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We use a discrete-time dynamical feedback system model of TCP/RED to study the performance of Random Early Detection (RED) for different values of control parameters. Our analysis shows that the queue length is able to keep stable at a given target if the maximum probability pmax and exponential averaging weight w satisfy some conditions. From the mathematical analysis, a new self-tuning RED is proposed to improve the performance of TCP-RED network. The appropriate pmax is dynamically obtained according to history information of both pmax and the average queue size in a period of time. And w is properly chosen according to a linear stability condition of the average queue length. From simulations with ns-2, it is found that the self-tuning RED is more robust to stabilize queue length in terms of less deviation from the target and smaller fluctuation amplitude, compared to adaptive RED, Random Early Marking (REM), and Proportional-Integral (PI) controller.