A NEURAL-FUZZY CONGESTION CONTROLLER FOR ATM NETWORKS

Abstract

We propose the use of a neural-fuzzy scheme for a rate-based feedback controller in ATM(Asynchronous Transfer Mode) networks. ABR (Available Bit Rate) traffic is not guaranteed quality of service (QoS) in the setup connection, and it can dynamically share the available bandwidth. Therefore, congestion can be controlled by regulationg the source rate to a certain degree depending on the current traffic flow. Traditional methods perform congestion control by monitoring the queue length. The source rate is decreased by a fixed rate when the queue length is greater than a prespecified threshold. However, it is difficult to get a suitable rate according to the degree of traffic congestion. We employ a neural-fuzzy mechanism to control the source rate. Through learning,membership values can be generated and cell loss can be predicted from the currnet queue length. Then an explicit rate is calculated and the source rate is controlled appropriately. Simulation results have shown that our method provides a better adaptive capability and a higher throughput than traditional methods.