On chaotic prediction and application to resource allocation strategies

The fractal nature of Internet traffic allows extending the application of the nonlinear chaotic system theory to the traffic control in modern telecommunication networks. In particular, the prediction techniques developed for these systems provide teletraffic engineers with a novel powerful tool for designing optimized traffic control algorithms. In this framework, the paper presents the performance evaluation of the Radial Basis Function Predictor (RBFP) in predicting actual traffic data. Predictor parameters are selected automatically by minimizing a suitably defined metric of prediction accuracy. The prediction system is then exploited in a simple resource allocation strategy to test the performance improvement achievable whenever a prediction of the future traffic intensity is available. The results obtained by means of discrete event simulation using actual traffic data are encouraging and stimulate a further-investigation of this approach.