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

Bashar, Abul, Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132, McClean, Sally, Scotney, Bryan and Nauck, D. (2009) BARD: A Novel Application of Bayesian Reasoning for Proactive Network Management. In: Proceedings of the 10th PostGraduate Symposium on the Convergence of Telecommunications, Networking and Broadcasting. PGNet.

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Abstract

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:	Book Section
Uncontrolled Keywords:	network management,next generation networks (ngn),bayesian belief networks (bbn),machine learning
Faculty \ School:	Faculty of Science > School of Computing Sciences
UEA Research Groups:	Faculty of Science > Research Groups > Smart Emerging Technologies Faculty of Science > Research Groups > Cyber Security Privacy and Trust Laboratory Faculty of Science > Research Groups > Data Science and AI
Depositing User:	Pure Connector
Date Deposited:	16 Nov 2016 13:00
Last Modified:	10 Dec 2024 01:11
URI:	https://ueaeprints.uea.ac.uk/id/eprint/61385
DOI:

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