On the Generation of Self-similar with Long-range Dependent Traffic Using Piecewise Affine Chaotic One-dimensional Maps (Extended Version)

EasyChair Preprint 5255, version 1

Abstract

A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long-range dependence (LRD) is presented by means of the formulation of a model that considers the use of piecewise affine one-dimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.

Keyphrases: Chaos, Hurst exponent, Traffic modeling in computer networks, chaotic maps, self-similarity

@booklet{EasyChair:5255,
  author    = {Ginno Millán},
  title     = {On the Generation of Self-similar with Long-range Dependent Traffic Using Piecewise Affine Chaotic One-dimensional Maps (Extended Version)},
  howpublished = {EasyChair Preprint 5255},
  year      = {EasyChair, 2021}}