Applications of neurocomputing in traffic management of ATM networks

Abstract

In the near future, high speed integrated networks, employing asynchronous transfer mode (ATM) cell switching and multiplexing technique, will be used to provide new and diverse mixture of services and applications. Multimedia teleconferencing, video-on-demand, television broadcasting, and distant learning are some examples of these emerging services. The ATM technique is based on the principle of statistical multiplexing, which is flexible enough to support different types of traffic while providing efficient utilization of the network's resources. New classes of techniques such as neural networks and fuzzy logic have many adaptive, learning and computational capabilities that can be utilized to design effective traffic management algorithms. The subject of this paper is to demonstrate how such neurocomputing techniques can be used to address ATM traffic management issues such as traffic characterization, call admission control, usage parameters control and feedback congestion control.