A simple estimator of the Hurst exponent for self-similar traffic flows

In this paper it presents, develops and discusses a new and simple algorithm based on the local maximum likelihood estimator of Whittle, for the estimation of the Hurst exponent of stationary second order time series representing the self-similar with long-range dependence traffic flows presents in the high-speed computer networks. This algorithm reduces the computational cost associated with the calculation of the local maximum likelihood estimator of Whittle reducing the amount of points to evaluate advantage the convexity of the objective function in the interest interval (0.5, 1), and applying a bisection search method over the derivate of the function. The instance is exposed with the intention of being considered as a new and alternative approach for modeling and simulating traffic in existing computer networks.