A neurocomputing controller for bandwidth allocation in ATM networks

We propose a new neurocomputing call admission control (CAC) algorithm for asynchronous transfer mode (ATM) networks. The proposed algorithm employs neural networks (NNs) to calculate the bandwidth required to support multimedia traffic with multiple quality-of-service (QoS) requirements. The NN controller calculates the bandwidth required percall using on-line measurements of the traffic via its count process, instead of relying on simple parameters such as the peak, average bit rate and burst length. Furthermore, to enhance the statistical multiplexing gain, the controller calculates the gain obtained from multiplexing multiple streams of traffic supported on separate virtual paths (i.e., class multiplexing). In order to simplify the design and obtain a small reaction time, the controller is realized using a hierarchical structure of a bank of small size, parallel NN units. Each unit is a feed-forward back-propagation NN that has been trained to, learn the complex nonlinear function relating different traffic patterns and QoS, with the corresponding received capacity. The reported results prove that the neurocomputing approach is effective in achieving more accurate results than other conventional methods that are based upon mathematical or simulation analysis. This is primarily due to the unique learning and adaptive capabilities of NNs that enable them to extract and memorize rules from previous experience. Evidently such unique capabilities poise NNs to solve many of the problems encountered in the development of a coherent ATM traffic management strategy