Using Bayesian Belief Networks for Burst Detection in Ethernet Passive Optical Networks

Naghmeh Moradpoor, Abul Bashar, Gerard Parr, Sally McClean, Bryan Scotney, G Owusu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The Ethernet Passive Optical Networks (EPONs) have been considered as a promising candidate for the next generation wired access networks for quite some time. In EPONs bandwidth requests and bandwidth allocations are critical issues which need to be addressed efficiently in order to guarantee the End-to-End (ETE) Quality of Service (QoS) for diverse classes of services. In this paper, we discuss the application of a statistical prediction technique based on the Bayesian Belief Networks (BBNs) which provides real-time decision support to the EPONs Dynamic Bandwidth Allocation (DBA) function. We show that in situations of burst traffic it helps the DBA to predict additional bandwidth requirements.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Number of pages5
Publication statusPublished - May 2011
EventInternational Conference on Wireless and Optical Communications - Zhengzhou, China
Duration: 1 May 2011 → …

Conference

ConferenceInternational Conference on Wireless and Optical Communications
Period1/05/11 → …

Keywords

  • EPON
  • BBN
  • DBA

Fingerprint Dive into the research topics of 'Using Bayesian Belief Networks for Burst Detection in Ethernet Passive Optical Networks'. Together they form a unique fingerprint.

  • Cite this

    Moradpoor, N., Bashar, A., Parr, G., McClean, S., Scotney, B., & Owusu, G. (2011). Using Bayesian Belief Networks for Burst Detection in Ethernet Passive Optical Networks. In Unknown Host Publication IEEE.