Learning-Based Call Admission Control Framework for QoS Management in Heterogeneous Networks

  • Abul Bashar
  • Gerard Parr
  • Sally McClean
  • Bryan Scotney
  • Detlef Nauck
Part of the Communications in Computer and Information Science book series (CCIS, volume 88)


This paper presents a novel framework for Quality of Service (QoS) management based on the supervised learning approach, Bayesian Belief Networks (BBNs). Apart from proposing the conceptual framework, it provides solution to the problem of Call Admission Control (CAC) in the converged IP-based Next Generation Network (NGN). A detailed description of the modelling procedure and the mathematical underpinning is presented to demonstrate the applicability of our approach. Finally, the theoretical claims have been substantiated through simulations and comparative results are provided as a proof of concept.


Quality of Service (QoS) Call Admission Control (CAC) Bayesian Belief Networks (BBNs) Next Generation Network (NGN) 


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  1. 1.
    ITU-T Y.2001: General overview of NGN. ITU-T Recommendation (2004) Google Scholar
  2. 2.
    Pras, A., et al.: Key research challenges in network management. IEEE Communications Magazine, 104–110 (2007)Google Scholar
  3. 3.
    Wright, S.: Admission control in multi-service IP networks. IEEE Communications Surveys Tutorials, 72–86 (2007)Google Scholar
  4. 4.
    Harrington, D., Presuhn, R., Wijnen, B.: An architecture for describing SNMP management frameworks. RFC 3411, IETF (2002)Google Scholar
  5. 5.
    ITU-T X.711: Open systems interconnection (OSI) common management information protocol: specification. ITU-T Recommendation (1997) Google Scholar
  6. 6.
    Kulkarni, P.G., McClean, S.I., Parr, G.P., Black, M.M.: Deploying MIB data mining for proactive network management. In: Proc. 3rd Intl. IEEE Conference on Intelligent Systems, pp. 506–511 (2006)Google Scholar
  7. 7.
    Sohail, S., Khanum, A.: Simplifying network management with fuzzy logic. In: IEEE Itnl. Conf. on Communications, pp. 195–201 (2008)Google Scholar
  8. 8.
    Hood, C.S., Ji, C.: Proactive network fault detection. IEEE Transactions on Reliability, 333–341 (1997)Google Scholar
  9. 9.
    Ding, J., Kramer, B., et al.: Predictive fault management in the dynamic environment of IP network. In: Proc. IEEE International Workshop on IP Operations and Management, pp. 233–239 (2004)Google Scholar
  10. 10.
    Sterritt, R., Marshall, A.H., Shapcott, C.M., McClean, S.I.: Exploring dynamic Bayesian belief networks for intelligent fault management systems. In: Proc. IEEE International Conference on Systems, Man and Cybernetics, pp. 3646–3652 (2000)Google Scholar
  11. 11.
    Ekaette, E.U., Far, B.H.: A framework for distributed fault management using intelligent software agents. In: Proc. IEEE Canadian Conference on Electrical and Computer Engineering 2003, vol. 2, pp. 797–800 (2003)Google Scholar
  12. 12.
    Sterritt, R., Bustard, D.W.: Fusing hard and soft computing for fault management in telecommunications systems. IEEE Transactions on Systems, Man and Cybernetics 32(2), 92–98 (2002)CrossRefGoogle Scholar
  13. 13.
    Korb, K.B., Nicholson, A.E.: Bayesian Artificial Intelligence. Chapman & Hall /CRC Press (2003)Google Scholar
  14. 14.
    Pearl, J.: Probabilistic reasoning in intelligent systems. Morgan Kaufmann, San Mateo (1988)Google Scholar
  15. 15.
    Tanenbaum, A.S.: Computer Networks. Prentice Hall, India (2003)Google Scholar
  16. 16.
    Yang, C., Reddy, A.V.S.: A taxonomy for congestion control algorithms in packet switching networks. IEEE Network, 34–45 (1995)Google Scholar
  17. 17.
    Opnet Modeler,
  18. 18.
    Jensen, F.V.: Bayesian networks and decision graphs, p. 69. Springer, New York (2001)zbMATHGoogle Scholar
  19. 19.
    Hugin Lite 6.9,
  20. 20.
    Lauritzen, S.L., Spiegelhalter, D.J.: Local computations with probabilities on graphical structures and their application to expert systems. Journal of the Royal Statistical Society, Series B (Methodological) 50(2), 157–224 (1988)zbMATHMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Abul Bashar
    • 1
  • Gerard Parr
    • 1
  • Sally McClean
    • 1
  • Bryan Scotney
    • 1
  • Detlef Nauck
    • 2
  1. 1.School of Computing and EngineeringUniversity of UlsterColeraineUK
  2. 2.Research and Technology, British Telecom, Adastral ParkIpswichUK

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