An intelligent architecture for traffic controls in ATM network

An asynchronous transfer mode (ATM) network is a high-speed multimedia network which handles various kinds of traffic with different bit-rates and different qualities of service (QoS). In order to maintain the QoS for each traffic source and to avoid possible congestion problems, an ATM network requires highly sophisticated and flexible controllers to insure that this demanding performance can be achieved under unexpected changes in traffic conditions. In this paper, we propose an intelligent architecture using recurrent neural networks and an expert system for traffic control in ATM networks. This traffic control using neural networks is suitable for ATM because neural networks can learn the traffic characteristics and the dynamic changes in the traffic. The proposed mechanism is based on the adaptive prediction of the future values of the traffic and the flow rate for each traffic source. At every given time slot, the controllers in the proposed architecture predict whether the congestion will happen or not and regulate the volume of input traffic for each traffic source before the congestion happens, maintaining the user-required QoS for each traffic source based on the predefined rules. Consequently, the mechanism guarantees the QoS for each traffic source and efficiently prevents congestion.