K-factor Gegenbauer ARMA process for network traffic simulation

The k-factor Gegenbauer ARMA process has been a widely used tool for modeling and predicting financial time series. This model has the ability to capture both short-range and long-range dependencies (SRD and LRD, respectively). Since the high-speed network traffic exhibits both of these dependencies, GARMA could be used for its modeling and prediction. In this paper, we present a simple parameter estimation procedure for the k-factor GARMA model. We also discuss the synthesis scheme for this model. The estimation procedure is used to model an MPEG video traffic data. Several synthetic traffic data are generated by varying the GARMA model parameters. The main characteristic of the synthetic traffic, self-similarity, is tested and compared to that of the actual MPEG data and the traffic synthesized using the autoregressive (AR) model. The results show that the k-factor GARMA model captures the main traffic characteristics in both time and frequency domain. They also show that the synthetic traffic data using the k-factor GARMA model are the closest to the actual data.