Wavelet-based analysis of hurst parameter estimation for self-similar traffic

In order to guarantee quality of service (QoS) over Internet, traffic analysis, traffic management have been active research areas. A lot of facts show the Internet traffic and variable bit rate videos streaming all are characterized by self-similar property. Hurst parameter as an important factor that reflects the self-similar property is a key to traffic management and QoS. In this paper existing wavelet methods for the estimation of the Hurst parameter of self-similar traffic is systematically analyzed and examined. The effects of wavelet functions, vanishing moments and wavelet decomposition levels to the results of wavelet methods for acquiring the Hurst parameter are investigated via numerical experiments. Some useful conclusions are drawn on the relationship between the accuracy of the methods and the selection of the order of vanishing moments and the selection of wavelet functions.