Application of Bayesian Networks for Autonomic Network Management

Abul Bashar, Gerard Parr, Sally McClean, Bryan Scotney, Detlef Nauck

Research output: Contribution to journalArticle

9 Citations (Scopus)

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.
LanguageEnglish
Pages174-207
JournalJournal of Network and Systems Management
Volume22
Issue number2
DOIs
Publication statusPublished - 2014

Fingerprint

Network management
Bayesian networks
Learning systems
Access control
Artificial intelligence
Next generation networks
Telecommunication networks
Managers
Computer systems
Management system
Machine learning
Admission control

Cite this

@article{3c12591322664ead998eddbfff771487,
title = "Application of Bayesian Networks for Autonomic Network Management",
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.",
author = "Abul Bashar and Gerard Parr and Sally McClean and Bryan Scotney and Detlef Nauck",
year = "2014",
doi = "10.1007/s10922-013-9289-x",
language = "English",
volume = "22",
pages = "174--207",
journal = "Journal of Network and Systems Management",
issn = "1064-7570",
number = "2",

}

Application of Bayesian Networks for Autonomic Network Management. / Bashar, Abul; Parr, Gerard; McClean, Sally; Scotney, Bryan; Nauck, Detlef.

In: Journal of Network and Systems Management, Vol. 22, No. 2, 2014, p. 174-207.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Application of Bayesian Networks for Autonomic Network Management

AU - Bashar, Abul

AU - Parr, Gerard

AU - McClean, Sally

AU - Scotney, Bryan

AU - Nauck, Detlef

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

U2 - 10.1007/s10922-013-9289-x

DO - 10.1007/s10922-013-9289-x

M3 - Article

VL - 22

SP - 174

EP - 207

JO - Journal of Network and Systems Management

T2 - Journal of Network and Systems Management

JF - Journal of Network and Systems Management

SN - 1064-7570

IS - 2

ER -