Optimum feedback strategy for access control mechanism modelled as stochastic differential equation in computer network

We consider optimum feedback control strategy for computer communication network, in particular, the access control mechanism. The dynamic model representing the source and the access control system is described by a system of stochastic differential equations developed in our previous works. Simulated annealing (SA) was used to optimize the parameters of the control law based on neural network. This technique was found to be computationally intensive. In this paper, we have proposed to use a more powerful algorithm known as recursive random search (RRS). By using this technique, we have been able to reduce the computation time by a factor of five without compromising the optimality. This is very important for optimization of high-dimensional systems serving a large number of aggregate users. The results show that the proposed control law can improve the network performance by improving throughput, reducing multiplexor and TB losses, and relaxing, not avoiding, congestion.