Ulster University Logo

Ulster Institutional Repository

Employing Bayesian Belief Networks for Energy Efficient Network Management

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

Bashar, Abul, Subramanian, Mani, Chaudhari, Santosh, Parr, Gerard, Gonsalves, Timothy, McClean, Sally and Bryan, Scotney (2010) Employing Bayesian Belief Networks for Energy Efficient Network Management. In: National Conference on Communications, Chennai, India. IEEE. 5 pp. [Conference contribution]

[img]
Preview
PDF
227Kb

DOI: 10.1109/NCC.2010.5430172

Abstract

Network management systems (NMS) are used to monitor the network and along with Operations Support Systems (OSS) maintain the performance with a focus on guaranteeing sustained QoS to the applications and services. One aspect that is given less importance is the energy consumption of the network elements during the off peak periods. This paper looks at a scenario where the NMS plays an important role in making the network energy efficient by intelligently turning the network elements or their selective ports to sleep mode when they are underutilized. To this end, we propose and evaluate a Bayesian Belief Network (BBN) based Decision Management System (DMS), which provides intelligent decisions to the NMS for it to adaptively alter the operational modes of the network elements, without compromising the performance and QoS constraints. Simulated network has been used to provide the proof of concept followed by discussions on the amount of energy saved, the effect on the network performance and the computational complexity of implementation of the solution.

Item Type:Conference contribution (Paper)
Keywords:Bayesian Belief Networks (BBN) , Energy-aware , Network Management
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:21993
Deposited By:Dr Cathryn Peoples
Deposited On:08 May 2012 16:12
Last Modified:08 May 2012 16:12

Repository Staff Only: item control page