Machine learning based Call Admission Control approaches: A comparative study

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

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

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.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages431-434
Number of pages4
DOIs
Publication statusPublished - 17 Jan 2011
EventInternational Conference on Network and Service Management (CNSM), 2010 - Niagara Falls, ON, Canada
Duration: 17 Jan 2011 → …

Conference

ConferenceInternational Conference on Network and Service Management (CNSM), 2010
Period17/01/11 → …

Fingerprint

Congestion control (communication)
Learning systems
Quality of service
Bayesian networks
Neural networks
Experiments

Keywords

  • Bayesian Networks
  • Call Admission Control
  • Machine Learning
  • Neural Networks
  • Quality of Service

Cite this

Bashar, Abul ; Parr, Gerard ; McClean, Sally ; Bryan, Scotney ; Nauck, Detlef. / Machine learning based Call Admission Control approaches: A comparative study. Unknown Host Publication. 2011. pp. 431-434
@inproceedings{298ee055af014a48af0ff59480582207,
title = "Machine learning based Call Admission Control approaches: A comparative study",
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.",
keywords = "Bayesian Networks, Call Admission Control, Machine Learning, Neural Networks, Quality of Service",
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note = "Reference text: General overview of NGN, ITU-T Recommendation Y.2001, Dec 2004. E. Alpaydin, Introduction to Machine Learning, MIT Press, 2004. J. Qi et. al, {"}Artificial intelligence applications in the telecommunication industry,{"} in Expert Systems, vol. 24, pp. 271-291, Sep. 2007. (Pubitemid 47283814) T.T.T. Nguyen, G. Armitage, {"}A survey of techniques for internet traffic classification using machine learning,{"} IEEE Communications Surveys & Tutorials, vol.10, no.4, pp.56-76, Fourth Quarter 2008. OPNET Modeler 16.0, http://www.opnet.com Hugin Researcher 7.3, http://www.hugin.com IBM SPSS Modeler 13.0, http://www.spss.com Resource and admission control functions in next generation networks, ITU-T Recommendation Y.2111, Nov 2008. H. G. Perros, K. M. Elsayed, {"}Call admission control schemes,{"} IEEE Communications Magazine, pp. 82-91, Nov 1996. A. Hiramatsu, {"}ATM communications network control by neural networks,{"} IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 122-130, Mar 1990. (Pubitemid 20689516) C. Huang, Y. Chuang, and D. Yang, {"}Implementation of call admission control scheme in next generation mobile communication networks using particle swarm optimization and fuzzy logic systems,{"} Expert Systems with Applications, vol. 35, vo. 3, pp. 1246-1251, Oct 2008. P. Guo, M. Zhang, Y. Jiang, J. Ren, {"}Policy-based QoS control using call admission control and SVM,{"} in Proc. of Pervasive Computing and Applications, ICPCA 2007, pp. 685-688, July 2007. (Pubitemid 351165761) A. Bashar, G. Parr, S. McClean, B. Scotney, and D. Nauck, {"}Knowledge discovery using Bayesian network framework for intelligent telecommunication network management,{"} in Proc. of 4 R. Zhang, and A. Bivens, {"}Comparing the use of Bayesian networks and neural networks in response time modeling for service-oriented systems,{"} in Proc. of ACM Workshop on Service-Oriented Computing Performance, SOCP 2007, pp. 67-74, June 2007. (Pubitemid 47291386) S. Haykin, Neural networks and learning machines, 3 F. Jensen, Bayesian Networks and Decision Graphs, 2",
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Bashar, A, Parr, G, McClean, S, Bryan, S & Nauck, D 2011, Machine learning based Call Admission Control approaches: A comparative study. in Unknown Host Publication. pp. 431-434, International Conference on Network and Service Management (CNSM), 2010, 17/01/11. https://doi.org/10.1109/CNSM.2010.5691261

Machine learning based Call Admission Control approaches: A comparative study. / Bashar, Abul; Parr, Gerard; McClean, Sally; Bryan, Scotney; Nauck, Detlef.

Unknown Host Publication. 2011. p. 431-434.

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

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AB - 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.

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KW - Call Admission Control

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