An assessment of resource exploitation using artificial intelligence based traffic control strategies

We assess the application of artificial intelligence techniques to the complex problem of traffic control in ATM networks. The paper deals with the close link between call admission control and usage parameter control and proposes a simulation-based analysis to demonstrate how inefficiency on the part of policing affects bandwidth allocation. To take this into account, the paper proposes a framework for traffic control in which the CAC and policing functions are both based on artificial intelligence techniques, i.e. neural networks and fuzzy logic. In this way it is possible to train the neural network in such a way as to take into account the real behavior of the policer. As the results obtained show this allows us to implement traffic management strategies which can improve the exploitation of network resources