Modeling Correlated Proxy Web Traffic Using Fourier Analysis

We analyze the arrival rate of accesses to Web proxy caching servers. The results show that the data display strong periodic autocorrelation. The examined data sets show a consistent behavior in terms of having periods corresponding to daily and weekly cycles that can be explained in terms of daily and weekly cyclic behavior of Web users. While these results confirm the correlation in the network traffic noticed by other researchers, we emphasize that this correlation is periodic. A new approach is introduced to model data that exhibit such characteristics by a combination of Fourier and statistical analysis techniques. The source of high correlation in the data is shown to come from the periodic and hence the deterministic part. Synthesized data that results from this modeling approach is shown to have a long-range dependent and self-similar behavior.