Telecommunication systems are built with extensive redundancy and complexity to ensure robustness and quality of service. Such systems requires complex fault identification and management tools. Fault identification and management are generally handled by reducing the number of alarm events (symptoms) presented to the operating engineer through monitoring, filtering and masking. The goal is to determine and present the actual underlying fault. Fault management is a complex task, subject to uncertainty in the symptoms presented. In this paper two key fault management approaches are considered: (i) rule discovery to attempt to present fewer symptoms with greater diagnostic assistance for the more traditional rule based system approach and (ii) the induction of Bayesian Belief Networks (BBNs) for a complete "intelligent" approach. The paper concludes that the research and development of the two target fault management systems can be complementary.
- Expert System
- Network Management
- Fault Identification
- Fault Prediction
- Artificial Intelligence Technique
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M. Cheikhrouhou, P. Conti, R. Oliveira, J. Labetoulle. Intelligent agents in network management: A state-of-the-art. Networking and Information Systems J., 1, pp9–38, Jun 1998.
T. Oates. Fault identification in computer networks: A review and a new approach. T.R. 95-113, University of Massachusetts at Amherst, Computer Science Department, 1995.
C. Bournellis. Internet’ 95. Internet World, 6(11) pp47–52, 1995.
M. Cheikhrouhou, P. Conti, J. Labetoulle, K. Marcus, Intelligent Agents for Network Management: Fault Detection Experiment. In Sixth IFIP/IEEE International Symposium on Integrated Network Management, Boston, USA, May 1999.
Z. Wang, Model of network faults, In B. Meandzija, J. Westcott (Eds.), Integrated Network Management I., North Holland, Elsevier Science Pub. B.V., 1989.
T. Oates. Automatically Acquiring Rules for Event Correlation From Event Logs, Technical Report 97-14, University of Massachusetts at Amherst, Computer Science Dept, 1997.
R. Sterritt, A.H. Marshall, C.M. Shapcott, S.I. Mcclean, Exploring Dynamic Bayesian Belief Networks For Intelligent Fault Management Systems, IEEE Int. Conf. Systems, Man and Cybernetics, V, pp3646–3652, Sept. 2000.
G. Jakobson and M. Weissman. Alarm correlation. IEEE Network, 7(6):52–59, Nov. 1993.
D. Gürer, I. Khan, R. Ogier, R. Keffer, An Artificial Intelligence Approach to Network Fault Management, SRI International, Menlo Park, California, USA.
R. N. Cronk, P. H. Callan, L. Bernstein, Rule based expert systems for network management and operations: An introduction. IEEE Network, 2(5) pp7–23, 1988.
K. Harrison, A Novel Approach to Event Correlation, HP, Intelligent Networked Computing Lab, HP Labs, Bristol. HP-94-68, July, pp. 1–10, 1994.
I. Bratko, S. Muggleton, Applications of Inductive Logic Programming, Communications of the ACM, Vol. 38, no. 11, pp. 65–70, 1995.
J. Liebowitz, (ed.) Expert System Applications to Telecommunication,. John Wiley and Sons, New York, NY, USA, 1988.
B. Mintegrated, J. Westcott, (eds.) Network Management I, North Holland/IFIP, Elsevier Science Publishers B.V., Netherlands, 1989.
J.R. Wright, J.E. Zielinski, E. M. Horton. Expert Systems Development: The ACE System, In , pp45–72, 1988.
Gary M. Slawsky and D. J. Sassa. Expert Systems for Network Management and Control in Telecommunications at Bellcore, In , pp191–199, 1988.
Shri K. Goyal and Ralph W. Worrest. Expert System Applications to Network Management, In , pp 3–44, 1988.
C. Joseph, J. Kindrick, K. Muralidhar, C. So, T. Toth-Fejel, MAP fault management expert system, In , pp 627–636, 1989.
T. Yamahira, Y. Kiriha, S. Sakata, Unified fault management scheme for network troubleshooting expert system. In , pp 637–646, 1989.
U.M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, From Data Mining to Knowledge Discovery: An Overview, Advances in Knowledge Discovery & Data Mining, AAAI Press & The MIT Press: California, pp1–34, 1996.
R.J. Brachman, T. Anand, The Process of Knowledge Discovery in Databases: A Human-Centered Approach., Advances in Knowledge Discovery & Data Mining, AAAI Press & The MIT Press: California, pp37–57, 1996.
R. Uthurusamy, From Data Mining to Knowledge Discovery: Current Challenges and Future Directions, Advances in Knowledge Discovery & Data Mining, AAAI Press & The MIT Press: California, pp 561–569, 1996.
R. Sterritt, Discovering Rules for Fault Management, Proceedings of IEEE International Conference on the Engineering of Computer Based Systems (ECBS), Washington DC, USA, April 17–20, pp190–196, 2001.
R. Sterritt, Fault Management and Soft Computing, Proceedings of the International Symposium Soft Computing and Intelligent Systems for Industry, Paisley, Scotland, UK, June 26–29, 2001.
R. Sasisekharan, V. Seshadri, and S. M. Weiss. Proactive network maintenance using machine learning. In Proceedings of the 1994 Workshop on Knowledge Discovery in Databases, pp 453–462, 1994.
A. Danyluk, F. Provost. Small disjuncts in action: Learning to diagnose errors in the telephone network local loop. In Proceedings of the Tenth International Conference on Machine Learning, 1993.
Foster Provost, Andrea Danyluk, A Study of Complications in Real-world Machine Learning, TR NYNEX, 1996.
German Goldszmidt and Yechiam Yemini. Evaluating management decisions via delegation. In H. G. Hegering and Y. Yemini, editors, Integrated Network Management, III, pp247–257. Elsevier Science Publishers B.V., 1993.
Rodney M. Goodman, Barry Ambrose, Hayes Latin, and Sandee Finnell. A hybrid expert system/neural network traffic advice system. In H. G. Hegering and Y. Yemini, editors, Integrated Network Management, III, pp 607–616. Elsevier Science Publishers B.V., 1993.
Rodney M. Goodman and H. Latin. Automated knowledge acquisition from network management databases. In I. Krishnan and W. Zimmer, editors, Integrated Network Management, II, pp 541–549. Elsevier Science Publishers B.V., 1991.
Shri K. Goyal. Knowledge technologies for evolving networks. In I. Krishnan and W. Zimmer, editors, Integrated Network Management, II, pp 439–461. Elsevier Sci ence Publishers B.V., 1991.
K. Hatonen, M. Klemettinen, H. Mannila, P. Ronkainen, H. Toivonen, Knowledge Discovery from Telecommunication Network Alarm Databases, Proc. 12th Int. Conf. on Data Engineering (ICDE’96), pp. 115–122, 1996.
Oates, T., Jensen, D., and Cohen, P. R. (1998). Discovering rules for clustering and predicting asynchronous events. In Danyluk, A. Predicting the future: AI approaches to timeseries problems. Technical Report WS-98-07, AAAI Press, pp73–79, 1998.
M. Cheikhrouhou, P. Conti, R. Oliveira, and J. Labetoulle. Intelligent agents in network management: A state-of-the-art. Networking and Information Systems Journal, 1 pp 9–38, Jun 1998.
M. Cheikhrouhou, P. Conti, J. Labetoulle, K. Marcus, Intelligent Agents for Network Management: Fault Detection Experiment. In Sixth IFIP/IEEE International Symposium on Integrated Network Management (IM’99), Boston, USA, May 1999.
Y.A. Sekercioglu, A. Pitsillides, A. Vasilakos, Computational Intelligence in Management of ATM Networks: A survey of Current Research, Proc. ERUDIT Workshop on Application of Computational Intelligence Techniques in Telecommunication, London, 1999.
B. Azvine, N. Azarmi, K.C. Tsui, Soft computing-a tool for building intelligent systems, BT Technology Journal, vol. 14, no. 4 pp37–45, Oct. 1996.
T. Clarkson, Applications of Neural Networks in Telecommunications, Proc. ERUDIT Workshop on Application of Computational Intelligence Techniques in Telecommunication, London, UK, 1999.
H. Wietgrefe, K. Tochs, and et al. Using neural networks for alarm correlation in cellular phone networks. In the International Workshop on Applications of Neural Networks in Telecommunications, 1997.
R.D. Gardner and David A. Harle, Alarm Correrlation and Network Fault Resolution using the Kohonen Self-Organising Map, Globecom-97, 1997.
R. Sterritt, K. Adamson, M. Shapcott, D. Bell, F. McErlean, Using A.I. For The Analysis of Complex Systems, Proc. Int. Conf. Artificial Intelligence and Soft Computing, pp113–116, 1997.
R. Sterritt, W. Liu, Constructing Bayesian Belief Networks for Fault Management in Telecommunications Systems, 1st EUNITE Workshop on Computational Intelligence in Telecommunications and Multimedia at EUNITE 2001, pp 149–154, Dec. 2001.
J. Cheng, D.A. Bell, W. Liu, An algorithm for Bayesian network construction from data. Proceedings of the 6th International Workshop on Artificial Intelligence and Statistics (AI&STAT’97), 1997.
C. J. K. Chow, C. N. Liu, Approximating discrete probability distributions with dependence trees, IEEE Trans. Information Theory, Vol. 14(3), pp. 462–467, 1968.
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Sterritt, R. (2002). Facing Fault Management as It Is, Aiming for What You Would Like It to Be. In: Bustard, D., Liu, W., Sterritt, R. (eds) Soft-Ware 2002: Computing in an Imperfect World. Soft-Ware 2002. Lecture Notes in Computer Science, vol 2311. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46019-5_3
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