Abstract
Traffic behavior in a large-scale network can be viewed as a complicated non-linear system, so it is very difficult to describe the long-term network traffic behavior in a large-scale network. In this paper, according to the non-linear character of network traffic, the time series of network traffic is decomposed into trend component, period component, mutation component and random component by different mathematical tools. So the complicated traffic can be modeled with these four simpler sub-series tools. In order to check the decomposed model, the long-term traffic behavior of the CERNET backbone network is analyzed by means of the decomposed network traffic. The results are compared with ARIMA model. According to autocorrelation function and predicting error function, compounded model can get higher error precision to describe the long-term traffic behavior.
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Guang, C., Jian, G., Wei, D. (2005). A Time-Series Decomposed Model of Network Traffic. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_50
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DOI: https://doi.org/10.1007/11539117_50
Publisher Name: Springer, Berlin, Heidelberg
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