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BARD: A Novel Application of Bayesian Reasoning for Proactive Network Management

Bashar, Abul, Parr, Gerard, McClean, Sally, Scotney, Bryan and Nauck, D (2009) BARD: A Novel Application of Bayesian Reasoning for Proactive Network Management. In: 10th PostGraduate Conference on Telecoms, Networking and Broadcasting, Liverpool. PGNet. 6 pp. [Conference contribution]



In the context of next generation networks (NGN), there is a critical need to address the challenges facing the network management functions, to administer the converged infrastructure and services. One of the challenges is to model the unknown and probabilistic dependency relationships among the diverse network elements and adapt this model to capture the real-time network behavior. This paper proposes BARD (BAyesian Reasoner and Decision-maker), a proactive system to enhance the network performance management functions by use of machine learning technique called Bayesian Belief Networks (BBN). It exploits the predictive and diagnostic reasoning features of BBN to make accurate decisions for effective management. A case study is presented to demonstrate the application of this technique to the problem of Call Admission Control in a typical communication network. The simulation results provide the proof that BARD is an effective and feasible solution when applied for network management tasks, especially Quality-of-Service (QoS) management.

Item Type:Conference contribution (Paper)
Keywords:Network Management, Next Generation Networks (NGN), Bayesian Belief Networks (BBN), Machine Learning.
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Information Engineering
Research Institutes and Groups:Computer Science Research Institute
Computer Science Research Institute > Information and Communication Engineering
ID Code:22015
Deposited By: Dr Cathryn Peoples
Deposited On:08 May 2012 14:57
Last Modified:09 Dec 2015 11:04

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