Traffic behavior analysis and modeling of sub-networks

In this paper, the characteristics of sub-network traffic is analyzed from the correlation point of view. It is easy to see that the traffic histogram of a sub-network has a 24-hour seasonal variation due to daily usage behavior. The auto correlation factor (ACF) and partial auto correlation factor (PACF) tests are applied first to examine the correlation of the traffic among consecutive hours and the correlation with a specific hour. The seasonal auto-regressive integrated moving average (ARIMA) model is applied to characterize the above properties of the network traffic. Modeling performance is evaluated by examining the coincidence of the histogram and the moving average of traffic volume between the actual traffic collected from the network and the traffic generated by the proposed model. The experimental results illustrate that the proposed model can effectively capture traffic behaviors of the sub-network and can then be used as a suitable traffic model for analysis of Internet performance.