Machine learning based call Admission Control approaches: A comparative study

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

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

10 Citations (Scopus)


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 publicationProceedings of the 2010 International Conference on Network and Service Management, CNSM 2010
PublisherThe Institute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)978-1-4244-8909-1 , 978-1-4244-8908-4
ISBN (Print)978-1-4244-8910-7
Publication statusPublished - 2010
Event2010 International Conference on Network and Service Management - Niagara Falls, Canada
Duration: 25 Oct 201029 Oct 2010


Conference2010 International Conference on Network and Service Management
Abbreviated titleCNSM 2010
CityNiagara Falls

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