Neural-based Queueing System Modelling for Service Quality Estimation in B-ISDN Networks

This paper addresses an original scheme based on feedforward neural networks, aimed at modelling queueing systems fed with bursty traffic. A neural network is trained to anticipate the average number of waiting cells, the cell loss rate and the coefficient of variation of the cell inter-departure time, given the mean rate, the peak rate and the coefficient of variation of the cell inter-arrival time. Our long-term goal is the design of a preventive control strategy in B-ISDN networks based on distributed neural networks modelling each queueing system located at the input and output ports of the switching facilities. To illustrate the potential of neural networks for modelling queueing systems, a neural network is successfully trained to model OnOff/D/1/c, OnOff/OnOff/1/c and multi-OnOff/D/1/c queueing systems.