Ulster University Logo

Application of Bayesian Networks for Autonomic Network Management

Bashar, Abul, Parr, Gerard, McClean, Sally, Scotney, Bryan and Nauck, Detlef (2014) Application of Bayesian Networks for Autonomic Network Management. Journal of Network and Systems Management, 22 (2). pp. 174-207. [Journal article]

Full text not available from this repository.

URL: http://link.springer.com/article/10.1007%2Fs10922-013-9289-x

DOI: 10.1007/s10922-013-9289-x

Abstract

The ever evolving telecommunication networks in terms of their technology, infrastructure, and supported services have always posed challenges to the network managers to come up with an efficient Network Management System (NMS) for effective network management. The need for automated and efficient management of the current networks, more specifically the Next Generation Network (NGN), is the subject addressed in this research. A detailed description of the management challenges in the context of current networks is presented and then this work enlists the desired features and characteristics of an efficient NMS. It then proposes that there is a need to apply Artificial Intelligence (AI) and Machine Learning (ML) approaches for enhancing and automating the functions of NMS. The first contribution of this work is a comprehensive survey of the AI and ML approaches applied to the domain of NM. The second contribution of this work is that it presents the reasoning and evidence to support the choice of Bayesian Networks (BN) as a viable solution for ML-based NMS. The final contribution of this work is that it proposes and implements three novel NM solutions based on the BN approach, namely BN-based Admission Control (BNAC), BN-based Distributed Admission Control (BNDAC) and BN-based Intelligent Traffic Engineering (BNITE), along with the description of algorithms underpinning the proposed framework.

Item Type:Journal article
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:30877
Deposited By: Professor Sally McClean
Deposited On:21 Jan 2015 11:11
Last Modified:05 Mar 2015 15:42

Repository Staff Only: item control page