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O. Cappe, E. Moulines, A. PetroPulu et al., “Long-range dependence and heavy-tail modeling for teletraffic data,” IEEE Signal Processing Magazine, Special Issue on Analysis and Modeling of High-Speed Data Network Traffic,” Vol. 19, No. 5, pp. 14–27, May 2002.

has been cited by the following article:

• TITLE: An Approach to Dynamic Asymptotic Estimation for Hurst Index of Network Traffic

  AUTHORS: Xiaoyan MA, Hongguang LI

  KEYWORDS: Network Traffic, Hurst Index, Dynamic Asymptotic Estimation, Long-Range Dependence

  JOURNAL NAME: International Journal of Communications, Network and System Sciences, Vol.3 No.2, March 1, 2010

  ABSTRACT: As an important parameter to describe the sudden nature of network traffic, Hurst index typically conducts behaviors of both self-similarity and long-range dependence. With the evolution of network traffic over time, more and more data are generated. Hurst index estimation value changes with it, which is strictly consistent with the asymptotic property of long-range dependence. This paper presents an approach towards dynamic asymptotic estimation for Hurst index. Based on the calculations in terms of the incremental part of time series, the algorithm enjoys a considerable reduction in computational complexity. Moreover, the local sudden nature of network traffic can be readily captured by a series of real-time Hurst index estimation values dynamically. The effectiveness and tractability of the proposed approach are demonstrated through the traffic data from OPNET simulations as well as real network, respectively.