Simulation of Self-similarFlow Based on Fractal Gaussian Noise Method

DOI

10.2991/icsnce-18.2018.27How to use a DOI?

Keywords

Self-similarity; Traffic Flow; FGN; R/S

Abstract

The conventional network traffic flow models are mostly based on Poisson model . With the continuous development of network services, studies found that the actual network traffic has a long-range dependence (LRD) now and in a very long time , which is a kind of self-similarity. In this paper, RMD and Fourier algorithm were adopted to simulate and analyze a self-similar model of FGN. They generated the necessary sequence of self-similar traffic. Then the article uses R/S method to verify H value of the generated sequence of self-similar traffic in order to verify the self-similarity of the self-similar traffic sequence. The existence of self-similarity is verified by experiments, and the advantage and disadvantage of RMD and Fourier algorithm are analyzed.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Li Jie
AU  - Lu Ying
AU  - Tang Junyong
PY  - 2018/04
DA  - 2018/04
TI  - Simulation of Self-similarFlow Based on Fractal Gaussian Noise Method
BT  - Proceedings of the 2018 Second International Conference of Sensor Network and Computer Engineering (ICSNCE 2018)
PB  - Atlantis Press
SP  - 133
EP  - 137
SN  - 2352-538X
UR  - https://doi.org/10.2991/icsnce-18.2018.27
DO  - 10.2991/icsnce-18.2018.27
ID  - Jie2018/04
ER  -