fGn series to develop one-dimensional chaotic maos that generate self-similar LRD traffic on high-speed computer networks

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 onedimensional 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.

本研究提出了一种面向生成长程依赖（long-range dependence, LRD）自相似流量的混沌模型的定性与定量扩展方案，该方案通过构建一种采用分段仿射一维映射的模型得以实现。基于对所生成时间序列的分解处理，本文对赫斯特指数（Hurst exponent）的数值变化行为给出了合理解释，并验证了通过所提模型的参数对其实施调控的可行性。