Dynamics of temporal correlation in daily Internet traffic

In order to characterize the dynamics of self-similar behavior in daily Internet traffic, we analyze the time series of traffic volume for a 24-hour period in a wide-area Internet, by using detrended fluctuation analysis (DFA) - a well-known method of characterizing nonstationarity in a time series. We show that the estimated scaling exponent (which is directly related to the Hurst parameter) of traffic fluctuations has a dependency on the level of human activity for a time scale greater than 30s. Thus, the temporal correlation for traffic fluctuations is close to 1/f-noise during the day, and becomes weaker at night. This result suggests that Internet traffic cannot be modeled using the unique value of the Hurst parameter.