Temporal alarm pattern discovery in mobile telecommunication networks based on binary series analysis

• Autorzy: Mazdziarz A.
• Języki publikacji: EN

Abstrakty

EN

Highly-advanced systems, such as mobile telecommunication networks, characterized by increased complexity, make maintenance routines difficult. Amount of data to be analyzed in a short time during fault diagnosis of the mobile telecommunication networks strongly justifies the need to automate alarm correlation and root cause analysis. A major challenge in the establishment of alarm correlation is to determine how to reflect the alarm flow inertia. Thus, adequate temporal alarm pattern discovery methods should be used in fault diagnosis for correlation-related purposes. Automatic temporal alarm pattern discovery allows fast generation of root cause analysis hypotheses and supports effective troubleshooting of network problems. The process for fault propagation throughout the network is manifested by the time lag between the root-cause alarm and potentially linked symptoms, as well as weakening correlation strength with time. The paper presents a novel method for alarm correlation analysis in mobile telecommunication networks, based on binary series analysis. The method allows for discovery of causal relationship between alarms with dynamic alarm correlation window size estimation.

Słowa kluczowe

EN

mobile telecommunication network; fault diagnosis; root cause analysis; Dice similarity coefficient; Hamming distance; temporal pattern mining

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2018
• Tom: Vol. 47, no. 2
• Strony: 191--213
• Opis fizyczny: Bibliogr. 27 poz., rys.

Twórcy

autor

Mazdziarz A.

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

Bibliografia

• [1] Abreu, R., Zoeteweij, P. and van Gemund, A.J. (2009) Spectrum-based multiple fault localization. Proceedings of the 2009 IEEE/ACM International Conference on Automated Software Engineering. IEEE Computer Society.
• [2] Banerjee, D., Madduri, V. and Srivatsa, M. (2009) A Framework for Distributed Monitoring and Root Cause Analysis for Large IP Networks. 28th IEEE International Symposium on Reliable Distributed Systems September 2009. IEEE Computer Society, 246-255.
• [3] Bang-Jensen, J. and Gutin, G. (2009) Digraphs: Theory, Algorithms and Applications. 2nd edn., Springer, Heidelberg.
• [4] Bhaumik, S.K. (2010) Root cause analysis in engineering failures. Transactions of The Indian Institute of Metals, 63, (2-3), 297-299.
• [5] Bouillard, A., Junier, A., Ronot, B. (2013) Alarms correlation in telecommunication networks. [Research Report] RR-8321, INRIA.
• [6] Bouloutas, A., Calo, S., and Finkel, A. (1994) Alarm correlation and fault identification in communication networks. IEEE Transactions on Communications, 42, 2/3/4.
• [7] Choi, S.S., Cha, S.H., and Tappert, C.C. (2015) A survey of binary similarity and distance measures. Department of Computer Science, Pace University New York, US.
• [8] Datta, R., Niharika, N. (2013) Comparative study between the generations of mobile communication 2G, 3G & 4G, International Journal on Recent and Innovation Trends in Computing and Communication, 1 (4).
• [9] EMCWhitePaper (2009) Automating Root-Cause Analysis: EMC Ionix Codebook Correlation Technology vs. Rules-based Analysis. November 2009, EMC White Paper Technology Concepts and Business Considerations.
• [10] Hubalek, Z. (1982) Coefficients of association and similarity, based on binary (presence-absence) data: An evaluation. Biological Reviews, 57(4), 669689.
• [11] Jian, W. and Ming, L.X. (2008) A novel algorithm for dynamic mining of association rules. International Workshop on Knowledge Discovery and Data Mining Jan 2008. IEEE, 94-99.
• [12] Jousselme, A.L. and Maupin, P. (2012) Distances in evidence theory: Comprehensive survey and generalizations. International Journal of Approximate Reasoning, 53, 118-145.
• [13] Jukic, O. and Kunstic, M. (2009) Logical inventory database integration into network problems frequency detection process. ConTEL 2009, June 2009. IEEE, 361-365.
• [14] Kim, D.S., Shinbo, H., Yokota, H. (2011) An Alarm Correlation Algorithm for Network Management Based on Root Cause Analysis. KDDI R&D Laboratories Inc. Fujimino Saitama Japan ICACT2011.
• [15] Lewis, L. (1995) Managing Computer Networks - A Case-Based Reasoning Approach, Telecommunications Library Artech House, Inc.
• [16] Mazdziarz, A. (2018) Alarm correlation in mobile telecommunication networks based on k-means cluster analysis method. Journal of Telecommunications and Information Technology (JTIT), 2/2018, 95-102.
• [17] Niederreiter, H. and Winterhof, A. (2015) Applied Number Theory. Springer, DOI: 10.1007/978-3-319-22321-6,102.
• [18] Padalkar, S., Karsai, G., Biegl, C., Sztipanovits, J., Okuda, K., and Miyasaka, N. (1991) Real-time fault diagnosis. IEEE Expert, 75-85.
• [19] Raghavan, A. (2015) Root cause analysis. Management and Leadership - A Guide for Clinical Professionals. Springer Verlag, 105-121.
• [20] Salah, S., Macia-Fernandez, G., and Diaz-Verdejo, J.E. (2013) A model-based survey of alert correlation techniques. Computer Networks, Elsevier, 1289-1317.
• [21] Samba, A. (2006) A Network Management Framework for Emerging Telecommunications Networks. Modeling and Simulation Tools for Emerging Telecommunication Networks Needs, Trends, Challenges and Solutions. Springer, Boston, MA.
• [22] Steinder, M., Sethi, A.S. (2004) A survey of fault localization techniques in computer networks. Science of Computer Programming, 53, 165-194.
• [23] Vaarandi, R. (2003) A data clustering algorithm for mining patterns from event logs. IP Operations and Management(IPOM 2003), October 2003. IEEE, 119-126.
• [24] Vaarandi, R. (2005) Tools and techniques for event log analysis. PhD thesis, Tallinn University of Technology, Department of Computer Engineering , Estonia, June 2005.
• [25] Warrens, M.J. (2008) Similarity coefficients for binary data. Properties of coefficients, coefficient matrices, multi-way metrics and multivariate coefficients. Doctoral dissertation, Leiden University, Netherlands.
• [26] Wijaya, S., Afendi, F.M., Latifah, I., Darusman, K., Altaf-Ul-Amin, M., and Kanaya, S. (2016) Finding an appropriate equation to measure similarity between binary vectors: case studies on Indonesian and Japanese herbal medicines. BMC Bioinformatics 17:520, DOI:10.1186/s 12859-016-1392-z.
• [27] Zeng, Ch., Tang, L., Li, T., Shwartz, L., Grabarnik, G.Y. (2014) Mining Temporal Lag from Fluctuating Events for Correlation and Root Cause Analysis, Network and Service Management (CNSM). 10th International Conference on Network and Service Management (CNSM) and Workshop. IEEE, 19-27, ISBN: 978-3-901882-67-8.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).