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

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.