Dynamic bandwidth allocation using a two-stage fuzzy neural network based traffic predictor

The work presents a predictive dynamic bandwidth allocation (PDBA) scheme that updates the allocated bandwidth periodically to minimize the queue´s build-up process. It requires an accurate traffic predictor so we use a two-stage predictor. The first stage includes FARIMA and FNN models running in parallel to predict the traffic data. While FARIMA captures the self-similarity, FNN captures the non-stationarity. The second stage combines the two forecasts using FNN to enhance the prediction accuracy. The performances of the PDBA scheme and the predictors are tested on MPEG and JPEG data. The results show that the two-stage predictor outperforms the individual ones. The proposed PDBA results in lower CLR compared to non-predictive schemes.