Queueing network modelling with distributed neural networks for service quality estimation in B-ISDN networks

We discuss a scheme based on distributed feedforward neural networks (NN), aimed at modelling several queueing systems in cascade fed with bursty traffic. For each queueing system, a neural network is trained to anticipate the average number of waiting packets, the packet loss rate and the coefficient of variation of the packet inter-departure time, given the mean rate, the peak rate and the coefficient of variation of the packet inter-arrival time. The latter serves for the calculation of the coefficient of variation of the cell inter-arrival time of the aggregated traffic which is fed as input to the next NN along the path. The potential of this method was sucessfully illustrated on several single server FIFO queues in Aussem et al. (1999). We now apply this technique to model a small queueing network made up from a combination of queues in tandem and in parallel fed by a superimposition of OnOff sources. Our long-term goal is the design of preventive control strategy in a multiservice communication network