Robust design of flow control in ATM network switching by using Gaussian neural networks

This paper deals with a design problem of flow patterns control at the level of asynchronous transfer mode (ATM) switching nodes. The bursty character of traffic patterns in ATM networks is modelled as multicommodity flows for multiple origin-destination networks and since the usual Markovian assumptions are unrealistic for ATM flow patterns, general distributions for the traffic streams and service times have to be considered. The Gaussian neural networks are used to approximate the dynamic map between the probability space of the general distributions (describing the flow clusters) and a finite set of parameters controlling the quality of service in the ATM network.