Abstract
| Language | English |
|---|---|
| Title of host publication | Unknown Host Publication |
| Pages | 431-434 |
| Number of pages | 4 |
| DOIs | |
| 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 → … |
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Keywords
- Bayesian Networks
- Call Admission Control
- Machine Learning
- Neural Networks
- Quality of Service
Cite this
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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 proceeding › Conference contribution
TY - GEN
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
ER -