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Machine learning based Call Admission Control approaches: A comparative study

Bashar, Abul, Parr, Gerard, McClean, Sally, Bryan, Scotney and Nauck, Detlef (2011) Machine learning based Call Admission Control approaches: A comparative study. In: International Conference on Network and Service Management (CNSM), 2010, Niagara Falls, ON, Canada. IEEE. 4 pp. [Conference contribution]


DOI: 10.1109/CNSM.2010.5691261


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.

Item Type:Conference contribution (Paper)
Keywords:Bayesian Networks , Call Admission Control , Machine Learning , Neural Networks , Quality of Service
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:21992
Deposited By: Dr Cathryn Peoples
Deposited On:08 May 2012 15:13
Last Modified:09 Dec 2015 11:04

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