Machine learning based Call Admission Control approaches: A comparative study

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

doi:10.1109/CNSM.2010.5691261

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 proceeding › Conference 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.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	431-434
Number of pages	4
ISBN (Print)	978-1-4244-8910-7
DOIs	https://doi.org/10.1109/CNSM.2010.5691261
Publication status	Published - 17 Jan 2011
Event	International Conference on Network and Service Management (CNSM), 2010 - Niagara Falls, ON, Canada Duration: 17 Jan 2011 → …

Conference

Conference	International Conference on Network and Service Management (CNSM), 2010
Period	17/01/11 → …

Keywords

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

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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). IEEE. https://doi.org/10.1109/CNSM.2010.5691261

@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",

author = "Abul Bashar and Gerard Parr and Sally McClean and Scotney Bryan and Detlef Nauck",

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; International Conference on Network and Service Management (CNSM), 2010 ; Conference date: 17-01-2011",

year = "2011",

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doi = "10.1109/CNSM.2010.5691261",

language = "English",

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T1 - Machine learning based Call Admission Control approaches: A comparative study

AU - Bashar, Abul

AU - Parr, Gerard

AU - McClean, Sally

AU - Bryan, Scotney

AU - Nauck, Detlef

N1 - 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

PY - 2011/1/17

Y1 - 2011/1/17

N2 - 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.

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.

KW - Bayesian Networks

KW - Call Admission Control

KW - Machine Learning

KW - Neural Networks

KW - Quality of Service

U2 - 10.1109/CNSM.2010.5691261

DO - 10.1109/CNSM.2010.5691261

M3 - Conference contribution

SN - 978-1-4244-8910-7

SP - 431

EP - 434

BT - Unknown Host Publication

PB - IEEE

T2 - International Conference on Network and Service Management (CNSM), 2010

Y2 - 17 January 2011

ER -