Bandwidth allocation of variable bit rate video in ATM networks using radial basis function neural networks

ATM supports a wide range of multimedia traffic. While ATM provides increased flexibility in supporting various types of traffic the traffic control problems become more difficult to solve when trying to achieve efficient use of network resources. One of such problem is the bandwidth allocation. This paper presents a new approach to the bandwidth allocation problem for video traffic using radial basis functions neural networks (RBFNN). Estimation of an accurate amount of bandwidth to support this traffic has been a challenging task using conventional algorithmic approaches. In this paper, we show that a radial basis function neural network (RBFNN) is capable of learning the non-linear multi-dimentional mapping between different multimedia traffic patterns quality of service (QoS) requirements and the required bandwidth to support each call. 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 low pass 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.