Generating traffic time series based on generalized cauchy process

Li, Ming and Lim, S. C. and Feng, Huamin (2007) Generating traffic time series based on generalized cauchy process. In: Computational Science – ICCS 2007. Lecture Notes in Computer Science (4487). Springer Berlin Heidelberg, pp. 374-381. ISBN 978-3-540-72583-1

[img] Text
Restricted to Repository staff only

Download (230kB)
Official URL:


Generating traffic time series (traffic for short) is important in networking, e.g., simulating the Internet. In this aspect, it is desired to generate a time series according to a given correlation structure that may well reflect the statistics of real traffic. Recent research of traffic modeling exhibits that traffic is well modeled by a type of Gaussian process called the generalized Cauchy (GC) process indexed by two parameters that separately characterize the self-similarity (SS), which is local property described by fractal dimension D, and long-range dependence (LRD), which is a global feature that can be measured by the Hurst parameter H, instead of using the linear relationship D = 2 − H as that used in traditional traffic model with a single parameter such as fractional Gaussian noise (FGN). This paper presents a computational method to generate series based on the correlation form of GC process indexed by 2 parameters. Hence, the present model can be used to simulate realizations that flexibly capture the fractal phenomena of real traffic for both short-term lags and long-term lags.

Item Type: Book Section
Additional Information: Book Subtitle: 7th International Conference, Beijing, China, May 27 - 30, 2007, Proceedings, Part I
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Engineering (FOE)
Depositing User: Ms Rosnani Abd Wahab
Date Deposited: 04 Feb 2014 04:23
Last Modified: 04 Feb 2014 04:23

Actions (login required)

View Item View Item