Nonlinear Neural Network Congestion Control Based on Genetic Algorithm for TCP/IP Networks

Active Queue Management (AQM) has been widely used for congestion avoidance in TCP networks. Although numerous AQM schemes have been proposed to regulate a queue size close to a reference level as RED, PI controller, PID Controller, Adaptive prediction controller (APC) and neural network using the Back-Propagation (BP) most of them are incapable of adequately adapting to TCP network dynamics due to TCP's non-linearity and time-varying stochastic properties. In this paper, we design a nonlinear neural network controller using the non-linear model of TCP network. Genetic algorithms are used to train the nonlinear neural controller. We evaluate the performances of the proposed neural network AQM approach via simulation experiments. The proposed approach yields superior performance with faster transient response, larger throughput, and higher link utilization, as compared to other schemes.