Alarm Correlation in Mobile Telecommunications Networks based on k-means Cluster Analysis Method

• Autorzy: Maździarz A.

Identyfikatory

DOI

10.26636/jtit.2018.124518

• Języki publikacji: EN

Abstrakty

EN

Event correlation and root cause analysis play a fundamental role in the process of troubleshooting all technical faults and malfunctions. An in-depth, complicated multiprotocol analysis can be greatly supported or even replaced by a troubleshooting methodology based on data analysis approaches. The mobile telecommunications domain has been experiencing rapid development recently. Introduction of new technologies and services, as well as multivendor environment distributed across the same geographical area create a lot of challenges in network operation routines. Maintenance tasks have been recently becoming more and more complicated, time consuming and require big data analyses to be performed. Most network maintenance activities are completed manually by experts using raw network management information available in the network management system via multiple applications and direct database queries. With these circumstances considered, identification of network failures is a very difficult, if not an impossible task. This explains why effective yet simple tools and methods providing network operators with carefully selected, essential information are needed. Hence, in this paper efficient approximated alarm correlation algorithm based on the k-means cluster analysis method is proposed.

Słowa kluczowe

EN

alarm correlation; alarm patterns; cluster analysis; mobile telecommunication network; root cause analysis

• Wydawca: Instytut Łączności - Państwowy Instytut Badawczy
• Czasopismo: Journal of Telecommunications and Information Technology
• Rocznik: 2018
• Tom: nr 2
• Strony: 95--102
• Opis fizyczny: Bibliogr. 25 poz., rys., tab.

Twórcy

autor

Maździarz A.

• Systems Research Institute, Polish Academy of Science, Newelska 6, 01-447 Warsaw, Poland

Bibliografia

• [1] M. Lopa and J. Vora, “Evolution of mobile generation technology: 1G to 5G and review of upcoming wireless technology 5G”, Int. J. of Modern Trends in Engineer. and Research, vol. 2, no. 10, pp. 281–290, 2015.
• [2] K. Singh, S. Thakur, and S. Singh, “Comparison of 3G and LTE with other generation”, Int. J. of Comput. Applic., vol. 121. no. 6, pp. 42-47, 2015.
• [3] A. Kumar, J. Sengupta, and Y. Liu, “3GPP LTE: the future of mobile broadband”, Wirel. Person. Commun., vol. 62, pp. 671–686, 2012 (doi: 10.1007/s11277-010-0088-3).
• [4] M. Steinder and A. S. Sethi, “A survey of fault localization techniques in computer networks”, Science of Comput. Program. vol. 53, pp. 165–194, 2004 (doi: 10.1016/j.scico.2014.01.010).
• [5] S. K. Bhaumik, “Root cause analysis in engineering failures”, Transact. of the Ind. Instit. of Metals, vol. 63, no. 2–3, pp. 297–299, 2010 (doi: 10.1007/s12666-010-0040-y).
• [6] A. Bouillard, A. Junier, and B. Ronot “Alarms correlation in telecommunication networks”, INRIA, pp. 17, 2013, [Online]. Available: https://hal.inria.fr/hal-00838969 (accessed on May 7, 2018).
• [7] A. Samba, “A Network management framework for emerging telecommunications networks”, in Modeling and Simulation Tools for Emerging Telecommunication Networks. Needs, Trends, Challenges and Solutions, A. N. Ince and E. Topuz, Eds. New York: Springer, 2006.
• [8] P. Hong and P. Sen, “Incorporating non-deterministic reasoning in managing heterogeneous network faults”, in Proc. 2nd IFIP/IEEE Int. Symp. on Integrated Network Manag., Washington, DC, USA, 1991.
• [9] M. T. Sutter and P. E. Zeldin, “Designing expert systems for real time diagnosis of self-correcting networks”, IEEE Network, vol. 2, no. 5, pp. 43–51, 1998 (doi: 10.1109/65.17979) .
• [10] A. Jain, “Data clustering: 50 years beyond k-means”, Pattern Recog. Let., vol. 31, no. 8, pp. 651–666, 2010 (doi: 10.1016/j.patrec.2009.09.011).
• [11] H. Steinhaus, “Sur la division des corp materiels en parties”, Bullet. of the Polish Acad. of Sciences, vol. 4, no. 12, pp. 801–804, 1956 [in French].
• [12] X. Wu, et. al. “Top 10 algorithms in data mining”, Knowl. and Infor. Sys., vol. 14, no. 1, pp. 1–37, 2007 (doi: 10.1007/s10115-007-0114-2).
• [13] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. New York: Springer, Series in Statistics, 2001.
• [14] S. T. Wierzchoń and M. A. Kłopotek, Algorithms of Cluster Analysis, Warsaw: Wyd. IPI PAN, 2015.
• [15] A.-L. Jousselme and P. Maupin, “Distances in evidence theory: Comprehensive survey and generalizations”, Int. J. of Approx. Reason., vol. 53, no. 2 pp. 118–145, 2012 (doi: 10.1016/j.ijar.2011.07.00C).
• [16] J. Koronacki and J. Ćwik, Statystyczne systemy uczące się. Warszawa: Exit, 2008 [in Polish].
• [17] L. Morissette and S. Chariter, “The k-means clustering technique: General considerations and implementation in Mathematica”, Tutor. in Quantit. Methods for Psychol., vol. 9, no. 1, pp. 15–24, 2013 (doi: 10.20982/tqmp.09.1.p015).
• [18] J. MacQueen, “Some methods for classifications and analysis of multivariate observations”, in Proc. of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, Volume 1: Statistics, L. M. Le Cam and J. Neyman, Eds. Berkeley, CA, USA: University of California Press, pp. 281–297, 1967.
• [19] J. A. Hartigan and M. A. Wong, “Algorithm AS 136: A K-means clustering algorithm”, J. of the Royal Statis. Soc. Series C (Appl. Statistics), vol. 28, pp. 100–108, 1979, (doi: 10.2307/2346830).
• [20] E. W. Forgy, “Cluster analysis of multivariate data: efficiency vs. interpretability of classifications”, Biometrics, vol. 21, pp. 768–769, 1965.
• [21] S. P. Lloyd, “Least squares quantization in PCM”, Transact. on Infor. Theory, vol. 28, no. 2, pp. 128–137, 1982 (doi: 10.1109/TIT.1982.1056489).
• [22] R documentation help of kmeans function [Online]. Available: https://www.rdocumentation.org/packages/stats/versions/3.5.0/topics/ kmeans (accessed on May 7, 2018).
• [23] M. Telgarsky and A. Vattani, “Hartigan’s Method: k-means clustering without Voronoi”, in Proc. 13th Int. Conf. on Artif. Intel. and Statistics AISTATS, Chia, Sardinia, Italy, 2010.
• [24] G. H. Ball and D. J. Hall, ISODATA: a novel method of data analysis and pattern classification. Menlo Park, CA: Stanford Research Institute, 1965.
• [25] Pattern recognition, A. Pinz, T. Pock, H. Bischof, and F. Leberl, Eds. New York: Springer, 2012 (doi: 10.1007/978-3-642-32717-9).