Neural networks based traffic prediction for cell discarding policy
Lin Hsiou-Ping, Yen‐Chieh Ouyang · Proceedings of International Conference on Neural Networks (ICNN'97) · 2002
Traditional ATM cell discarding policies have some limitations. They are either difficult to implement or lack flexibility. In this paper, we proposed a new cell discarding policy that is based on the traffic load prediction by time-delayed neural networks. We use the finite-duration impulse response (FIR) filter in the multilayer neural networks to determine which cells will be discarded when the network buffer is going to overflow. The simulation uses ten different sources to generate cells according to their respective characteristic. The number of learning iterations, the normalized squared sum prediction error of the multilayer neural network are measured. The goodput is used to evaluate the performance of the proposed cell discarding policy. From the simulation result, the proposed cell discarding policy can achieve high goodput value that is near optimal.