A Network Traffic Prediction Algorithm Based On Data Mining Technique

Authors: D. Prangchumpol

Abstract:

This paper is a description approach to predict incoming and outgoing data rate in network system by using association rule discover, which is one of the data mining techniques. Information of incoming and outgoing data in each times and network bandwidth are network performance parameters, which needed to solve in the traffic problem. Since congestion and data loss are important network problems. The result of this technique can predicted future network traffic. In addition, this research is useful for network routing selection and network performance improvement.

Keywords: Traffic prediction, association rule, data mining.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1087275

References:

[1] Y. Wen, and T. Lee, “Fuzzy data mining and grey recurrent neural network forecasting for traffic information systems,” in Proc. IEEE International Conference on Information Reuse and Integration, pp. 356-361.
[2] Y. Hand, and F. Moutarde, “Analysis of Network-level Traffic States using Locality Preservative Non-negative Matrix Factorization,” in Proc. 14th IEEE Intelligent Transport Systems Conference (ITSC'2011), Washington : United States,2011.
[3] T. Hauser, and W. Scherer, “Data mining tools for real time traffic signal decision support and maintenance,” in Proc. IEEE International Conference on Systems, 2001, 3: 1471-1477.
[4] B. Park, D. Lee, and H. Yun, “Enhancement of time of day based traffic signal control,” in Proc. IEEE International Conference on Systems, 2003,4: 3619-3624.
[5] Xu, P., and S. Lin, “Internet traffic classification using C4.5 decision tree,”. J. Softw., vol.20(10), pp. 2692-2704, 2009.
[6] L. Jia, L. Yang, Q. Kong, and S. Lin, “ Study of artificial immune clustering algorithm and its applications to urban traffic control,” Int. J. Inform. Technol., 2006, vol.12, pp.1-9.
[7] B. Raahemi, A. Kouznetsov, A. Hayajneh, and P. Rabinovitch, “Classification of peer-to-peer traffic using incremental neural networks (fuzzy ARTMAP),” in Proc. IEEE Canadian Conference on Electrical and Computer Engineering, 2008, pp. 719-724.
[8] Z. Li, X. Yan, C. Yuan, J. Zhao ,and Z. Peng, “The fault diagnosis approach for gears using multidimensional features and intelligent classifier,” Imeche. Sem. Worldwide, vol.41, pp. 76-86, 2010.
[9] Z. Li, X. Yan, C. Yuan, J. Zhao, and Z. Peng, “Fault detection and diagnosis of the gearbox in marine propulsion system based on bispectrum analysis and artificial neural networks,” J. Mar. Sci. Appl., ,vol.10, pp. 17-24, 2011.
[10] Z. Li, X. Yan, C. Yuan, Z. Peng, and L. Li, “Virtual prototype and experimental research on gear multi-fault diagnosis using waveletautoregressive model and principal component analysis method,” Mech. Syst. Signal Pr ., vol. 25, pp.2589-2607, 2011.
[11] Z. Li, X. Yan, Y. Jiang, L. Qin ,and J. Wu, “A new data mining approach for gear crack level identification based on manifold learning,” Mechanika,vol 18, pp.29-34, 2012.
[12] Li, Z., X. Yan, Z. Guo, P. Liu, C. Yuan ,and Z. Peng, “A new intelligent fusion method of multi-dimensional sensors and its application to tribosystem fault diagnosis of marine diesel engines,” Tribol. Lett., vol.47, pp. 1-15,2012.
[13] Li, Z., X. Yan, C. Yuan, and Z. Peng, “Intelligent fault diagnosis method for marine diesel engines using instantaneous angular speed,” J. Mech. Sci. Technol., vol. 26(8), pp. 2413-2423, 2012.
[14] M.J.A Berry, and G. S. Linnoff, “Data Mining Techniques for Marketing, Sale and Customer Relationship Management,” New York: Wiley Publishing, 2004.
[15] D. Ng’ambi “Pre_empting User Questions through Anticipation- Data Mining FAQ Lists,” in Proc. of SAICSIT,2002,pp.101-109.
[16] N. Feamste, and J. Rexford, “Network-Wide BGP Route Prediction for Traffic Engineering”. a Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA Internet and Networking Systems, AT&T Labs. Research, Florham Park, NJ, USA, 2004.
[17] J. Han, and M. Kamber, “Data Mining Concepts and Techniques,” USA : Morgan Kaufman,2001.