A novel approach to the estimation of the Hurst parameter in self-similar traffic

We present a new method to estimate the Hurst parameter of the increment process in network traffic-a process that is assumed to be self-similar. The confidence intervals and biasedness are obtained for the estimates using the new method. This new method is then applied to pseudo-random data and to real traffic data. We compare the performance of the new method to that of the widely-used wavelet method, and demonstrate that the former is much faster and produces much smaller confidence intervals of the Hurst parameter estimate. We believe that the new method can be used as an on-line estimation tool for H and thus be exploited in the new TCP algorithms that exploit the known self-similar and long-range dependent nature of network traffic.