fGn series to develop one-dimensional chaotic maps 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.

本研究通过构建考虑分段线性一维映射的模型，对生成具有长程依赖性（LRD）的自相似交通流所用的混沌模型进行了定性与定量的扩展。基于对生成的时间序列的分解，提出了对赫斯特指数值行为的有效解释，并展示了从所提模型参数中控制其可行性的方法。