Machine learning based Call Admission Control approaches: A comparative study

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

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

Abstract

The importance of providing guaranteed Quality of Service (QoS) cannot be overemphasised, especially in the NGN environment which supports converged services on a common IP transport network. Call Admission Control (CAC) mechanisms do provide QoS to class-based services in a proactive manner. However, due to the factors of complexity, scale and dynamicity of NGN, Machine Learning techniques are favoured to analytical approaches for providing autonomous CAC. This paper is an effort to compare the performance of two such approaches - Neural Networks (NN) and Bayesian Networks (BN), to model the network behaviour and to estimate QoS metrics to be used in the CAC algorithm. It provides a way to find the optimum model training size for accurate predictions. Performance comparison is based on a wide range of experiments through a simulated network in Opnet. The outcome of this comparative study provides some interesting insights into the behaviour of NN and BN models and how they can be utilised for better CAC implementations.
Original languageEnglish
Title of host publicationUnknown Host Publication
PublisherIEEE
Pages431-434
Number of pages4
ISBN (Print)978-1-4244-8910-7
DOIs
Publication statusPublished - 17 Jan 2011
EventInternational Conference on Network and Service Management (CNSM), 2010 - Niagara Falls, ON, Canada
Duration: 17 Jan 2011 → …

Conference

ConferenceInternational Conference on Network and Service Management (CNSM), 2010
Period17/01/11 → …

Bibliographical note

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Keywords

  • Bayesian Networks
  • Call Admission Control
  • Machine Learning
  • Neural Networks
  • Quality of Service

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