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Research Papers

Prediction of Congestion and Bursting Phenomena in Network Traffic Based on Multifractal Spectrums

[+] Author and Article Information
Yan Liu

School of Mechanical Engineering,
Northwestern Polytechnical University,
Xi'an 710072, PRC;
Xi'an Shaangu Power Co. Ltd.,
Xi'an 710075, PRC
e-mail: liuyan@nwpu.edu.cn

Hang Wang

Xi'an Shaangu Power Co. Ltd.,
Xi'an 710075, PRC

Contributed by the Dynamic Systems Division of ASME for publication in the Journal of Dynamic Systems, Measurement, and Control. Manuscript received March 30, 2011; final manuscript received July 8, 2012; published online March 28, 2013. Assoc. Editor: Bor-Chin Chang.

J. Dyn. Sys., Meas., Control 135(3), 031012 (Mar 28, 2013) (7 pages) Paper No: DS-11-1093; doi: 10.1115/1.4023665 History: Received March 30, 2011; Revised July 08, 2012

From the viewpoint of nonlinear dynamics, a numerical method for predicting the traffic of the local area network (LAN) is presented based on the multifractal spectrums; in particular, for predicting the typical congestion and bursting phenomena, by analyzing real time sequences. First, the multifractal spectrums available to the LAN traffic are derived in some detail and their physical meanings are consequently explained. Then an exponent factor is introduced to the measurement or description of the singularity of the time sequence and the correlations between multifractal spectrums and traffic flow rate are studied in depth. Finally, as an example, the multifractal spectrums presented are used to predict the network traffic of an Ethernet by analyzing its real time sequence. The results show that there exists a distinct relationship between the multifractal spectrums and the traffic flow rate of networks and the multifractal spectrum could be used to efficiently and feasibly predict the traffic flow rate, especially for predicting the singularities of the real time sequences, which are closely related to the congestion and bursting phenomena. Thus, this method can be applied to the prediction and management of the congestion and bursting in the network traffic at an early time. Furthermore, the prediction will become much more accurate and powerful over a long period, since the fluctuations of the traffic flow rate are remarkable.

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References

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Figures

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Fig. 1

Multifractal spectrum for real network traffic

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Fig. 2

Structure function for real network traffic

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Fig. 3

Weighting factors and multifractal spectrum parameters (Monday)

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Fig. 4

Distribution of Zi versus Δαi and Δfi

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Fig. 5

Changing of G with Δfi

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Fig. 6

Changing of G with Δfi−1

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Fig. 7

Changing of G with Δf i−2

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Fig. 8

Changing of G with Δf i−3

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Fig. 9

Changing of G with Δf i−1 + Δf i−2

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Fig. 10

Changing of G with Δf i−1 + Δf i−2 + Δf i−3

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