MCMC simulation of GARCH model to forecast network traffic load

The performance of a computer network can be enhanced by increasing number of servers, upgrading the hardware, and gaining additional bandwidth but this solution require the huge amount to invest. In contrast to increasing the bandwidth and hardware resources, network traffic modeling play a significant role in enhancing the network performance. As the emphasis of telecommunication service providers shifted towards the high-speed networks providing integrated services at a prescribed Quality of Service (QoS), the role of accurate traffic models in network design and network simulation becomes ever more crucial. We analyze a traffic volume time series of internet requests made to a workstation. This series exhibits a long-range dependence and self-similarity in large time scale and exhibits multifractal in small time scale. In this paper, for this time series, we proposed Generalized Autoregressive Conditional Heteroscedastic, (GARCH) model, and practical techniques for model fitting, Markov Chain Monte Carlo simulation and forecasting issues are demonstrated. The proposed model provides us simple and accurate approach for simulating internet data traffic patterns.