Recent measurement studies show that the burstiness of packet traffic in IAN as well as WAN is associated with long-range correlation that can be efficiently modeled as self-similar arrival process in terms ofaccuracy. Since Hurst index is the key value of this model replesenting the burstiness of traffic source

efficient estimation of Hurst index to the given accuracy is the basic step of flow control as well as buffer management in high speed broadband networks（e. g. ATM） with self-similar traffic. In this paper

we propose a fast Hurst index estimation method based on multiresolution sampling and wavelet analysis. The proposed method is the maximum likelihood estimator for strict self-similar model and can be used to check the consistency of Hurst index in different observed range of time. Simulation results based on fractal Gaussian noise and real traffic data reveal that the total number of count samples of the burst traffic for parameter estimation is peally reduced compared to the traditional methods such as R/S statistics and variance-time analysis. The proposed approach also shows more accuracy and robustness than traditional methods when lacking of sample data.Thus our method can be applied to the application of traffic enforcement and congestion control in ATM networks.