Modeling network traffic using generalized Cauchy process

被引:76
作者
Li, Ming [1 ]
Lim, S. C. [2 ]
机构
[1] E China Normal Univ, Sch Informat Sci & Technol, Shanghai 200241, Peoples R China
[2] Multimedia Univ, Fac Engn, Cyberjaya 63100, Selanger, Malaysia
基金
中国国家自然科学基金;
关键词
long-range dependence; Hurst parameter; fractal dimension; self-similarity; generalized Cauchy process; network traffic; time series;
D O I
10.1016/j.physa.2008.01.026
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
Processes with long-range dependence (LRD) have gained wide applications in many fields of science and technologies ranging from hydrology to network traffic. Two key properties of such processes are LRD that is characterized by the Hurst parameter H and self-similarity (SS) that is measured by the fractal dimension D. However, in the popular traffic model using fractional Gaussian noise (fGn), these two parameters are linearly related. This may be regarded as a limitation of fGn in traffic modeling from the point of view of either accurately fitting real traffic or appropriately explaining the particular multi-fractal phenomena of traffic. In this paper, we discuss recent results in traffic modeling from a view of the generalized Cauchy (GC) process. The GC process is indexed by two parameters D and H. The parameter D in the GC model is independent of H. Hence, it provides a more flexible way to describe the multi-fractal phenomena of traffic in addition to accurately modeling traffic for both short-term lags and long-term ones. (c) 2008 Elsevier B.V. All rights reserved.
引用
收藏
页码:2584 / 2594
页数:11
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