Machine learning based call Admission Control approaches: A comparative study

Bashar, Abul, Parr, Gerard ORCID: https://orcid.org/0000-0002-9365-9132, McClean, Sally, Scotney, Bryan and Nauck, Detlef (2010) Machine learning based call Admission Control approaches: A comparative study. In: Proceedings of the 2010 International Conference on Network and Service Management, CNSM 2010. The Institute of Electrical and Electronics Engineers (IEEE), CAN, pp. 431-434. ISBN 978-1-4244-8910-7

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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: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Depositing User: Pure Connector
Date Deposited: 24 Sep 2016 01:07
Last Modified: 22 Dec 2022 14:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/60530
DOI: 10.1109/CNSM.2010.5691261

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