Radial basis functions for bandwidth estimation in ATM networks

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 challenge task using conventional algorithmic approaches. In this paper, we show that a radial basis function neural networks (RBFNN) is 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, 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 which is determined such that the error in estimating the bandwidth is minimized to less than 3% of the actual value. 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 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. Evidently, such unique capabilities poise NN to solve many of the problems encountered in the design of ATM networks.