Employing Bayesian Belief Networks for Energy Efficient Network Management

Abul Bashar, Mani Subramanian, Santosh Chaudhari, Gerard Parr, Timothy Gonsalves, Sally McClean, Scotney Bryan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

11 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Number of pages5
ISBN (Print)978-1-4244-6383-1
DOIs
Publication statusPublished - 15 Mar 2010
EventNational Conference on Communications - Chennai, India
Duration: 15 Mar 2010 → …

Conference

ConferenceNational Conference on Communications
Period15/03/10 → …

Bibliographical note

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Available at: http://www.hugin.com

Keywords

  • Bayesian Belief Networks (BBN)
  • Energy-aware
  • Network Management

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