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Machine learning based Call Admission Control approaches: A comparative study

Basher, Abul, Parr, Gerard, McClean, Sally, Bryan, Scotney and Nauck, Detlef (2010) 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 Press. 4 pp. [Conference contribution]

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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)
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:19092
Deposited By: Professor Sally McClean
Deposited On:18 Jul 2011 15:13
Last Modified:18 Jul 2011 15:13

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