Multi-scale network traffic prediction using k-factor Gegenbauer ARMA and MLP models

Summary form only given. High-speed network traffic prediction is an essential step in building effective preventive congestion control schemes. This paper addresses the problem of high-speed network traffic prediction at different timescales. We propose a two-stage mixture model where the first stage includes two individual models, namely k-factor Gegenbauer ARMA (GARMA) and multilayer perceptron (MLP). The k-factor GARMA captures the short- and long-range dependency, whereas the MLP captures the nonstationarity. The second stage combines the two forecasts to enhance the prediction accuracy and merge the traffic characteristics captured by the individual models. Four different combination schemes are investigated. They are averaging, Karmarker´s linear programming algorithm, MLP combiner and fuzzy neural network. The performance is tested on four different real traffic data, MPEG video, JPEG video, Ethernet and Internet. The problem of one-step-ahead traffic prediction at different timescales is considered. The results indicate that the proposed two-stage mixture model outperforms the individual models. The results also show that the prediction performance depends on the traffic nature and the considered timescale.