Applying a recurrence plot scheme to analyze non-stationary transition patterns of IP-network traffic |
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| Authors: | Masao Masugi |
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| Institution: | 1. Department of Physics, University of North Bengal, Siliguri 734013, West Bengal, India;2. Department of Physics, Chakdaha College, Chakdaha, Nadia 741222, West Bengal, India;3. Department of Physics, Dinhata College, Dinhata, Cooch Behar 736135, West Bengal, India;4. Department of Computer and Information Sciences, SUNY at Fredonia, Fredonia, New York 14063, USA;1. Collaborative Innovation Center of South China Sea Studies, Nanjing University, Nanjing 210093, China;2. Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, Nanjing University, Nanjing 210023, China;3. Geographic and Oceangraphic Sciences, Nanjing University, Nanjing 210023, China;4. School of Management Science and Engineering, Guangxi University of Finance and Economics, Nanning 530003, China;5. College of Territorial Resources and Tourism, Anhui Normal University, Wuhu 241002, China;1. Department of Physics, Faculty of Science, University of Yaoundé I, P.O. Box 812, Yaoundé, Cameroon;2. Institute of Mathematics, University of Kassel, Heinrich-Plett Str. 40, 34132 Kassel, Germany;3. LE2I UMR 6306, CNRS, Arts et Métiers, University of Bourgogne Franche-Comté, F-21000 Dijon, France;1. Centre de Mathématiques Appliquées, Ecole Polytechnique, 91128 Palaiseau Cedex, France;2. Department of Mathematics, University of California, Irvine CA 92697 United States;3. Lusenn, 3 Place de l’Eglise, 29570 Roscanvel, France |
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| Abstract: | This paper describes a recurrence plot (RP) approach to the analysis of non-stationary transition patterns of IP-network traffic. To get a quantitative measure of dynamical transition patterns of IP-network traffic, we used the values of determinism (DET) defined by the recurrence quantification analysis (RQA). Also, when evaluating the fractal-based properties of IP-network traffic, we focused on two parameters: (i) the long-range dependence (LRD)-related scaling parameter α derived from the detrended fluctuation analysis (DFA) and (ii) the range of the generalized fractal dimension. In applying this method to traffic analysis, we performed two kinds of traffic measurement in Tokyo, Japan, and derived the values of DET and fractal-based parameters of IP-network traffic over time. In checking the measured network traffic, we found that the characteristic with respect to DET and self-similarity seen in the measured network traffic fluctuated over time, with different time-variation patterns for two measurement locations. Results also confirmed that a larger value of DET or accumulated DET reflected increases in the degree of LRD of IP-network traffic and that the accumulated DET reflected the decreases in the degree of multi-fractality of IP-network traffic. As a result, we confirmed that RP-based measures can be effective for evaluating the non-stationary transition patterns of IP-network traffic in terms of quantitative fractal-based properties. |
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