A novel neural network traffic descriptor for ATM networks

Accurate characterization of the multimedia traffic is essential, in asynchronous transfer mode (ATM) broadband networks, in order to develop a robust set of traffic descriptors. Such a set is required, by the usage parameter control (UPC) algorithm, for traffic enforcement (policing). The authors present a novel approach to characterize and model the multimedia traffic using neural networks (NNs). A backpropagation NN is used to characterize and predict the statistical variations of the packet arrival process resulting from the superposition of N packetized video sources and M packetized voice sources. The accuracy of the results is verified by matching the index of dispersion for counts (IDC), the variance, and the autocorrelation of the arrival process to those of the NN output. The results show that the NNs can be successfully utilized to characterize the complex nonrenewal process with extreme accuracy