Abstract
The ever-evolving nature of telecommunication networks has put enormous pressure on contemporary Network Management Systems (NMSs) to come up with improved functionalities for efficient monitoring, control and management. In such a context, the rapid deployments of Next Generation Networks (NGN) and their management requires intelligent, autonomic and resilient mechanisms to guarantee Quality of Service (QoS) to the end users and at the same time to maximize revenue for the service/network providers. We present a framework for evaluating a Bayesian Networks (BN) based Decision Support System (DSS) for assisting and improving the performance of a Simple Network Management Protocol (SNMP) based NMS. More specifically, we describe our methodology through a case study which implements the function of Call Admission Control (CAC) in a multi-class video conferencing service scenario. Simulation results are presented for a proof of concept, followed by a critical analysis of our proposed approach and its application.
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References
ITU-T: General overview of NGN, ITU-T Recommendation Y.2001 (2004)
Pras, A., et al.: Key research challenges in network management. IEEE Communications Magazine, 104–110 (2007)
Alpaydin, E.: Introduction to Machine Learning. MIT Press, Cambridge (2004)
Harrington, D., Presuhn, R., Wijnen, B.: An architecture for describing SNMP management frameworks. RFC 3411, IETF (2002)
ITU-T: TMN Management Functions, ITU-T Recommendation M.3400 (2000)
Kulkarni, P.G., McClean, S.I., Parr, G.P., Black, M.M.: Deploying MIB data mining for proactive network management. In: 3rd Intl. IEEE Conference on Intelligent Systems, pp. 506–511 (2006)
Sohail, S., Khanum, A.: Simplifying network management with fuzzy logic. In: IEEE Intl. Conf. on Communications, pp. 195–201 (2008)
Hood, C.S., Ji, C.: Proactive network fault detection. IEEE Transactions on Reliability, 333–341 (1997)
Ding, J., Kramer, B., et al.: Predictive fault management in the dynamic environment of IP network. In: IEEE International Workshop on IP Operations and Management, pp. 233–239 (2004)
Ekaette, E.U., Far, B.H.: A framework for distributed fault management using intelligent software agents. In: IEEE Canadian Conference on Electrical and Computer Engineering, vol. 2, pp. 797–800 (2003)
Hui, T.C.K., Chen-Khong, T.: Adaptive provisioning of differentiated services networks based on reinforcement learning. IEEE Transactions on Systems, Man, and Cybernetics 33(4), 492–501 (2003)
Bashar, A., Parr, G.P., McClean, S.I., Scotney, B.W., Nauck, D.: BARD: A novel application of Bayesian reasoning for proactive network management. In: 10th Annual Postgraduate Conference on Telecommunications, Networking and Broadcasting (PGNET 2009), pp. 161–166 (2009)
Bashar, A., et al.: Employing Bayesian belief networks for energy efficient network management. In: IEEE National Conference on Communications (NCC) 2010, pp. 1–5 (2010)
Qi, J., Wu, F., Li, L., Shu, H.: Artificial intelligence applications in the telecommunications industry. Expert Systems 24, 271–291 (2007)
Heckerman, D.: A tutorial on learning with Bayesian Networks. In: Jordan, M. (ed.) Learning in Graphical Models. MIT Press, Cambridge (1999)
Spirtes, P., Glymour, C., Scheines, R.: Causation, Prediction, and Search, 2nd edn. MIT Press, Cambridge (2001)
Steck, H.: Constrained-based structural learning in Bayesian networks using finite data sets. PhD Thesis, Institut für der Informatik der Technischen Universität München (2001)
Jensen, F.: Bayesian Networks and Decision Graphs, 2nd edn. Springer, Heidelberg (2007)
Murphy, K.: Dynamic Bayesian networks (2002), http://people.cs.ubc.ca/murphyk/Papers/dbnchapter.pdf
Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence, 1st edn. CRC Press, Boca Raton (2003)
Wright, S.: Admission Control in Multi-Service IP Networks: A Tutorial. IEEE Communications Surveys & Tutorials, 72–86 (2007)
OPNET Modeler 16.0, http://www.opnet.com
Hugin Researcher 7.2, http://www.hugin.com
Cisco Systems: Network element polling with Cisco active network abstraction (2007), http://www.ciscosystems.com/
Nam, S.Y., et al.: Measurement-based admission control at edge routers. IEEE/ACM Transactions on Networking 16(2), 410–423 (2008)
Nichols, K., et al.: Definition of the Differentiated Services Field (DS Field) in the IPv4 and IPv6 Headers. RFC 2474, IETF (1998)
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Bashar, A., Parr, G., McClean, S., Scotney, B., Nauck, D. (2010). Knowledge Discovery Using Bayesian Network Framework for Intelligent Telecommunication Network Management. In: Bi, Y., Williams, MA. (eds) Knowledge Science, Engineering and Management. KSEM 2010. Lecture Notes in Computer Science(), vol 6291. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15280-1_47
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DOI: https://doi.org/10.1007/978-3-642-15280-1_47
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