Adaptive QoS Resource Management by Using Hierarchical Distributed Classification for Future Generation Networks

  • Simon Fong
Part of the Communications in Computer and Information Science book series (CCIS, volume 162)


With the arrivals of 3G/4G mobile networks, a diverse and new range of applications will proliferate, including video-on-demand, mobile-commerce and ubiquitous computing. It is expected a sizable proportion of these traffics move along the networks. Resources in the networks will have to be divided between voice support and data support. For the data support, multiple classes of services from the new mobile applications that have different requirements have to be monitored and managed efficiently. Traditionally Quality-of-Service (QoS) resource management was done by manual estimation of resources to be allocated in traffic profiles in GSM/GPRS environment. The resource allocations parameters are adjusted only after some period of time. In this paper, we propose a QoS resource allocation model that dynamically monitors every aspect of the network environment according to a hierarchy of QoS requirements. The model can derive knowledge of the network operation, and may even pinpoint the cause, should any anomaly occurs or malfunctions in the network. This is enabled by a hierarchy of classifiers or decision-trees, built stream-mining technology. The knowledge from the classifiers is inferred by using reasoning-of-evidence theory, and it is used for subsequent resource allocation. By this way, the resources in the network will be more dynamically and accurately adjusted, and responsive to the fluctuating traffic demands.


QoS Resource Management Hierarchical Classifiers Stream-mining 


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  1. 1.
    Levine, D.A., Akyildiz, I.F., Naghshineh, M.: A Resource Estimation and Call Admission Algorithm for Wireless Multimedia Networks Using the Shadow Cluster Concept. IEEE/ACM Transactions on Networking (5) (1997)Google Scholar
  2. 2.
    El-Kadi, M., Olariu, S., Abdel-Wahab, H.: A Rate-based Borrowing Scheme for QoS Provisioning in Multimedia Wireless Networks. IEEE Transactions of Parallel and Distributed Systems (13) (2002)Google Scholar
  3. 3.
    Ye, J., Hou, J., Papavassiliou, S.: Comprehensive Resource Management Framework for Next Generation Wireless Networks. IEEE Transactions on Mobile Computing 4(1), 249–264 (2002)Google Scholar
  4. 4.
    Maniatis, S., Nikolouzou, E., Venieris, I.: QoS Issues in the Converged 3G Wireless and Wired Networks. IEEE Communications Magazine 8(40), 44–53 (2002)CrossRefGoogle Scholar
  5. 5.
    Chen, H., Zeng, Q.-A., Agrawal, D.P.: A Novel Analytical Model for Optimal Channel Partitioning in the Next Generation integrated Wireless and Mobile Networks. In: Proceedings of the 5th ACM International Workshop on Modeling Analysis and Simulation of Wireless and Mobile Systems, pp. 120–127 (2002)Google Scholar
  6. 6.
    Ahluwalia, P., Varshney, U.: A Link and Network Layer Approach To Support Mobile Commerce Transactions. In: IEEE 58th Vehicular Technology Conference, vol. (5), pp. 3410–3414 (2003)Google Scholar
  7. 7.
    Lai, E., Fong, S., Hang, Y.: Supporting Mobile Payment QOS by Data Mining GSM Network Traffic. In: The 10th International Conference on Information Integration and Web-based Applications & Services (iiWAS 2008), Linz, Austria, November 24-26, pp. 279–285. ACM, New York (2008) ISBN:978-1-60558-349-5Google Scholar
  8. 8.
    User requirements for next generation networks, D1.1.1, IST-2001-38835 ANWIRE (November 2002)Google Scholar
  9. 9.
    Fong, S., Lai, E.: Mobile mini-payment scheme using SMS-credit. In: Gervasi, O., Gavrilova, M.L., Kumar, V., Laganá, A., Lee, H.P., Mun, Y., Taniar, D., Tan, C.J.K. (eds.) ICCSA 2005. LNCS, vol. 3481, pp. 1106–1116. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  10. 10.
    Kosuga, M., Yamazaki, T., Ogino, N., Matsuda, J.: Adaptive QoS management using layered multi-agent system for distributed multimedia applications. In: International Conference on Parallel Processing, Japan, pp. 388–394 (1999)Google Scholar
  11. 11.
    Ecklund, D.J., Goebel, V., Plagemann, T., Ecklund Jr., E.F.: Dynamic end-to-end QoS management middleware for distributed multimedia systems. Special Issue on Multimedia Middleware, Multimedia Systems 8(5), 431–442Google Scholar
  12. 12.
    Nguyen, X.T.: Agent-Based QoS Management for Web Service Compositions, PhD Thesis, Swinburne University of Technology, Australia (June 2008)Google Scholar
  13. 13.
    Fong, S., Tang, A.: A Taxonomy-based Classification Model by Using Abtraction and Aggregation. In: The 2nd International Conference on Data Mining and Intelligent Information Technology Applications (ICMIA 2010), Seoul, Korea, November 30-December 2, pp. 448–454 (2010)Google Scholar
  14. 14.
    García-Borroto, M., Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A.: Cascading an emerging pattern based classifier. In: Martínez-Trinidad, J.F., Carrasco-Ochoa, J.A., Kittler, J. (eds.) MCPR 2010. LNCS, vol. 6256, pp. 240–249. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Sentz, K., Ferson, S.: Combination of Evidence in Dempster-Shafer Theory. In: SAND 2002-0835, pp.3–96 (April 2002)Google Scholar
  16. 16.
    Fay, R., Schwenker, F., Thiel, C., Palm, G.: Hierarchical neural networks utilising dempster-shafer evidence theory. In: Schwenker, F., Marinai, S. (eds.) ANNPR 2006. LNCS (LNAI), vol. 4087, pp. 198–209. Springer, Heidelberg (2006)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Simon Fong
    • 1
  1. 1.Faculty of Science and TechnologyUniversity of MacauMacau SAR

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