Novel distributed call admission control solution based on machine learning approach

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

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

1 Citation (Scopus)

Abstract

The advent of IP-based Next Generation Network (NGN) and its guaranteed QoS promise has attracted significant attention from both service providers and subscribers. However, to fulfil the said promise, there is a need to provide effective Call Admission Control (CAC) based QoS provisioning solutions which are autonomous, intelligent and scalable.
LanguageEnglish
Title of host publicationUnknown Host Publication
Pages871-881
Number of pages11
DOIs
Publication statusPublished - 18 Aug 2011
EventIFIP/IEEE International Symposium on Integrated Network Management - Dublin, Ireland
Duration: 18 Aug 2011 → …

Other

OtherIFIP/IEEE International Symposium on Integrated Network Management
Period18/08/11 → …

Fingerprint

Congestion control (communication)
Learning systems
Quality of service
Next generation networks

Keywords

  • Bayesian methods
  • Call admission control
  • Delay
  • Machine learning
  • Next generation networking
  • Predictive models
  • Support vector machines

Cite this

Bashar, Abul ; Parr, Gerard ; McClean, Sally ; Bryan, Scotney ; Nauck, Detlef. / Novel distributed call admission control solution based on machine learning approach. Unknown Host Publication. 2011. pp. 871-881
@inproceedings{f11642269db7422899b53eb468bf45aa,
title = "Novel distributed call admission control solution based on machine learning approach",
abstract = "The advent of IP-based Next Generation Network (NGN) and its guaranteed QoS promise has attracted significant attention from both service providers and subscribers. However, to fulfil the said promise, there is a need to provide effective Call Admission Control (CAC) based QoS provisioning solutions which are autonomous, intelligent and scalable.",
keywords = "Bayesian methods, Call admission control, Delay, Machine learning, Next generation networking, Predictive models, Support vector machines",
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, R. Boutaba, J, P. Martin-Flatin, J. L. Hellerstein, R. H. Katz, G. Pavlou, L. Chin-Tau, {"}Recent advances in autonomic communications [Guest Editorial],{"} In IEEE Journal on Selected Areas in Communications, vol. 28, no. 1, pp. 1-3, Jan. 2010. C. Yun and H. Perros, {"}QoS control for NGN: A Survey of Techniques,{"} In Journal of Network and Systems Management, vol. 18, no. 4, pp. 447-461, Feb. 2010. E. Alpaydin, Introduction to Machine Learning, MIT Press, 2004. Resource and admission control functions in next generation networks, ITU-T Recommendation Y.2111, Nov. 2008. D. Liu, Y. Zhang, H. Zhang, {"}A self-learning call admission control scheme for COMA cellular networks,{"} In IEEE Transactions on Neural Networks, vol.16, no. 5, pp. 1219-1228, Sep. 2005. F. R. Yu, V. W. S. Wong, V. C. M. Leung, {"}A new QoS provisioning method for adaptive multimedia in wireless networks,{"} In IEEE Transactions on Vehicular Technology, vol. 57, no. 3, pp. 1899-1909, May 2008. P. Guo, M. Zhang, Y. Jiang, J. Ren, {"}Policy-based QoS control using call admission control and SVM,{"} In Proc. of 2nd International Conference on Pervasive Computing and Applications (ICPCA 2007), pp. 685-688, Jul. 2007. B. Rong, Y. Qian, K. Lu, R. Q. Hu, M. Kadoch, {"}Mobile agent based handoff in wireless mesh networks: architecture and call admission control,{"} In IEEE Transactions on Vehicular Technology, vol. 58, no. 8, pp. 4565-4575, Oct. 2009. A. Bashar, G. P. Parr, S. I. McClean, B. W. Scotney, D. Nauck, {"}Learning-based call admission control framework for QoS management in heterogeneous networks,{"} In Proc. of Springer LNCS CCIS series, 2nd International Conference on Networked Digital Technologies (NDT 2010), vol. II, pp. 99-111, Jul. 2010. A. Bashar, G. P. Parr, S. I. McClean, B. W. Scotney, D. Nauck, {"}Machine Learning based call admission control approaches: A comparative study,{"} in Proc. of IEEE/IFIP 6th International Conference on Network and Service Management (CNSM 2010), Oct. 2010. K. B. Laskey, {"}MEBN: A Language for first-order Bayesian knowledge bases,{"} Artificial Intelligence, vol. 172, no. 2-3, pp. 140-178, Feb. 2008. J. Qi, F. Wu, L. Li, H. Shu, {"}Artificial intelligence applications in the telecommunication industry,{"} in Expert Systems, vol. 24, no. 4, pp. 271-291, Sep. 2007. Opnet Modeler 16.0, http://www.opneLcom Hugin Researcher 7.3, http://www.hugin.com",
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Bashar, A, Parr, G, McClean, S, Bryan, S & Nauck, D 2011, Novel distributed call admission control solution based on machine learning approach. in Unknown Host Publication. pp. 871-881, IFIP/IEEE International Symposium on Integrated Network Management, 18/08/11. https://doi.org/10.1109/INM.2011.5990495

Novel distributed call admission control solution based on machine learning approach. / Bashar, Abul; Parr, Gerard; McClean, Sally; Bryan, Scotney; Nauck, Detlef.

Unknown Host Publication. 2011. p. 871-881.

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

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KW - Call admission control

KW - Delay

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KW - Next generation networking

KW - Predictive models

KW - Support vector machines

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M3 - Conference contribution

SN - 978-1-4244-9219-0 (print)

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