Route optimization and traffic management in an ATM network using neural computing

摘要

Neural computing techniques are used to optimize route selection in a communication network, such as an ATM network. Output measurements of the network are used to provide optimal routing selection and traffic management. Specifically, link data traffic is monitored in the network to obtain traffic history data. An autoregressive backpropagation neural network is trained using the traffic history data to obtain respective predicted traffic profiles for the links. Particular links are then selected for carrying data based on the predicted traffic profiles. A cost function, limits on network parameters such as link cost and cell rate, and other quality of service factors are also considered in selecting the optimal route.