Effective Flow Control on Self-similar Traffic in ATM Networks An FIR Neural Network Approach

Abstract

This work presents a novel feedback rate regulator using the multiple leaky bucket (MLB) for VBR self-similar traffic that is based on the traffic load prediction by time-delayed neural networks in ATM networks. In contrast to the conventional leaky bucket (LB), the leak rate and buffer capacity of all LBs are shared in the same virtual path to more effectively utilize network resources. In the MLB mechanism, the leak rate and buffer capacity of each LB can be dynamically adjusted based on the buffer occupancy. A finite-duration impulse response (FIR) multilayer neural network is used to predict the incoming traffic load and pass the information to the feedback rate regulator. In addition, ten real world MPEG1 and ten synthesized traffic traces are used to validate the performance of the MLB and the MLB with FIR prediction mechanism. Simulation results demonstrate that the cell loss rate using MLB and MLB with FIR has a three to more than ten thousand time improvement over the conventional leaky bucket method.