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Estimation of Hurst Exponent in Self-similar Traffic Flows.

EasyChair Preprint no. 5159

9 pagesDate: March 16, 2021


In this paper it presents, develops and discusses the existence of a process with long scope memory structure, representing of the independence between the degree of randomness of the traffic generated by the sources and flow pattern exhibited by the network. The process existence is presented in term of a new algorithmic that is a variant of the maximum likelihood estimator (MLE) of Whittle, for the calculation of the Hurst exponent (H) of self-similar stationary second order time series of the flows of the individual sources and their aggregation. Also, it is discussed the additional problems introduced by the phenomenon of the locality of the Hurst exponent, that appears when the traffic flows consist of diverse elements with different Hurst exponents. 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.

Keyphrases: Hurst exponent (H), long-range dependence, Long-scope memory, Maximum Likelihood Estimator (MLE), self-similarity, Telematic traffic modeling, Whittle estimator

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Ginno Millán},
  title = {Estimation of Hurst Exponent in Self-similar Traffic Flows.},
  howpublished = {EasyChair Preprint no. 5159},

  year = {EasyChair, 2021}}
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