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Machine learning based Call Admission Control approaches: A comparative study

Biomedical Sciences Research Institute Computer Science Research Institute Environmental Sciences Research Institute Nanotechnology & Advanced Materials Research Institute

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]

Full text not available from this repository.

DOI: 10.1109/CNSM.2010.5691261

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.

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 16:13
Last Modified:18 Jul 2011 16:13

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