Novel distributed call admission control solution based on machine learning approach

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

doi:10.1109/INM.2011.5990495

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 proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Unknown Host Publication
Publisher	IEEE
Pages	871-881
Number of pages	11
ISBN (Print)	978-1-4244-9219-0 (print)
DOIs	https://doi.org/10.1109/INM.2011.5990495
Publication status	Published (in print/issue) - 18 Aug 2011
Event	IFIP/IEEE International Symposium on Integrated Network Management - Dublin, Ireland Duration: 18 Aug 2011 → …

Other

Other	IFIP/IEEE International Symposium on Integrated Network Management
Period	18/08/11 → …

Bibliographical 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

Keywords

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

Access to Document

10.1109/INM.2011.5990495

Cite this

@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; IFIP/IEEE International Symposium on Integrated Network Management ; Conference date: 18-08-2011",

year = "2011",

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day = "18",

doi = "10.1109/INM.2011.5990495",

language = "English",

isbn = "978-1-4244-9219-0 (print)",

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T1 - Novel distributed call admission control solution based on machine learning approach

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, 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

PY - 2011/8/18

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N2 - 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.

AB - 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.

KW - Bayesian methods

KW - Call admission control

KW - Delay

KW - Machine learning

KW - Next generation networking

KW - Predictive models

KW - Support vector machines

U2 - 10.1109/INM.2011.5990495

DO - 10.1109/INM.2011.5990495

M3 - Conference contribution

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

SP - 871

EP - 881

BT - Unknown Host Publication

PB - IEEE

T2 - IFIP/IEEE International Symposium on Integrated Network Management

Y2 - 18 August 2011

ER -

Novel distributed call admission control solution based on machine learning approach

Abstract

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Bibliographical note

Keywords

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