A Novel Approach to the Estimation of the Hurst Parameter in Self-Similar Traffic

We present a new method to estimate the Hurst pa-rameter of the increment process in network traffic – a process that is assumed to be self-similar. The con-fidence 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 traf-fic 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 pro-duces 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 ex-ploit the known self-similar and long-range dependent nature of network traffic.