Radial basis functions for bandwidth estimation in ATM networks using RBF neural network

It is known that some types of variable bit rate (VBR) video traffic exhibit strong long term correlations and non-stationary behavior. Estimation of an accurate amount of bandwidth to support this traffic has been a challenging task using conventional algorithmic approaches. We show that radial basis function neural networks (RBFNN) are capable of learning the non-linear multi-dimensional mapping between different video traffic patterns, quality of service (QoS) requirements and the required bandwidth to support each call. In addition, the RBFNN model adopts to new traffic scenarios and still produces accurate results. This approach bypass the modeling approach which requires detailed knowledge about the traffic statistical patterns. Our method employs "on-line" measurements of the traffic count process over a monitoring period. In order to simplify the design of the RBFNN, the input traffic is preprocessed through a lowpass filter in order to smooth all high frequency fluctuations. A large set of training data, representing different traffic patterns with different QoS requirements, was used to ensure that the RBFNN can generalize and produce accurate results when confronted with new data. The reported results prove that the neurocomputing approach is effective in achieving more accurate results than other traditional methods, based upon mathematical or simulation analysis. This is primarily due to the fact that the unique learning and adaptive capabilities of NN enable them to extract and memorize rules from previous experience.