Characterization of packetized voice traffic in ATM networks using neural networks

Asynchronous transfer mode (ATM) broadband networks support a wide range of multimedia traffic (e.g. voice, video, image and data). Accurate characterization of the multimedia traffic is essential in ATM networks, in order to develop a robust set of traffic descriptors. Such a set is required by the ATM networks for the important functions of traffic enforcement (policing) and bandwidth allocation utilizing the statistical multiplexing gain. In this paper, we present a novel approach to characterize and model the multimedia traffic using neural networks (NNs). A backpropagation NN is used to characterize the statistical variations of the packet arrival process resulting from the superposition of a number of packetized voice sources. The NN is trained to characterize the arrival process over a fixed measurement period of time, based upon sampled values taken from the previous measurement period. The accuracy of the results were verified by matching the index of dispersion for counts and the variance of the arrival process to those of the NN output. The results reported show that the NNs can be successfully utilized to characterize the complex non-renewal process resulting from the aggregate voice-packet arrival process with extreme accuracy. Hence, NNs have excellent potential as traffic descriptors for the usage parameter control algorithm used in admission control and traffic enforcement in ATM networks