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 p max â ¡ 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 p max â ¡ is dynamically obtained according to history information of both p max â ¡ 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.