Application of artificial neural networks to effective bandwidth estimation in ATM networks

A prime instrument for controlling congestion in ATM networks is admission control, which limits calls and guarantees a grade of service (GOS) determined by delay and loss probability in the multiplexer. It is essential for an admission control scheme to characterize, for a given GOS, the effective bandwidth requirement of the aggregate bandwidth usage of multiplexed connections. In this paper, an accurate and computationally efficient approach is proposed to estimate the effective bandwidth of multiplexed connections. In this method, a feedforward neural network is employed to model the complex relationship between the effective bandwidth and the traffic situations and a GOS measure. It is trained and tested via a large number of patterns generated by the accurate fluid flow model. Due to the neural network's adaptive learning, high computation rate and generalization features, this method can increase the link utilization and is suitable for real-time network traffic control applications.