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Self-Adaptive QoS Control Mechanism in Cognitive Networks Based on Intelligent Service Awareness

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Web Information Systems and Mining (WISM 2011)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6987))

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Abstract

Cognitive network is a network with a cognitive process that can perceive current network conditions, as well as plan, decide and act on those conditions. Through self-learning mechanism and adaptive mechanisms of the network, it achieves the end-end goals. This paper proposes self-adaptive QoS control mechanism in cognitive networks based on intelligent service awareness. In this architecture, network flow can be identified and classified by intelligent service-aware model. Drawing on Control Theory, network traffic can be controlled with a self-adaptive QoS control mechanism that has end-link collaboration in cognitive network. This mechanism can adjust resource allocation, adapt to a changeable network environment, optimize end-to-end performance of the network, and ensure QoS.

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References

  1. Lin, C., Shan, Z., Ren, F.: QoS of next generation networks. Chinese Journal of Computers 31(9), 1525–1535 (2008)

    Article  Google Scholar 

  2. Mitola, J., Maguire, G.: Cognitive radio: Making software radios more personal. IEEE Personal Commun. 69(8), 13–18 (1999)

    Article  Google Scholar 

  3. Clark, D., Partrige, C., Ramming, J., et al.: A knowledge plane for the Internet. In: Proc. Conf. on Applications, Tech., Architectures, and Protocols for Comput. Commun.(SIGCOMM 2003), pp. 3–10 (2003)

    Google Scholar 

  4. Baldo, N., Zorzi, M.: Fuzzy logic for cross-layer optimization in cognitive radio networks. IEEE Commun. Mag. 46(4), 64–71 (2008)

    Article  Google Scholar 

  5. Siebert, M.: Self-X control in (future) mobile radio networks. In: Proc. European-Chinese Cognitive Radio Syst. Workshop (2008)

    Google Scholar 

  6. Fortuna, C., Mohorcic, M.: Trends in the development of communication networks: cognitive networks. Computer Networks 53(9), 1354–1376 (2009)

    Article  Google Scholar 

  7. Shao, F., Wang, L.: Cognitive network structure and approach based on cognitive level. J. Beijing Univ. Tech. 35(4), 1181–1187 (2009)

    MathSciNet  Google Scholar 

  8. Thomas, R.: Cognitive Networks. Virginia Polytechnic and State University, Blacksburg (2007)

    Book  Google Scholar 

  9. Marco, B., Mellia, Pescape, A., Salgarelli, L.: Traffic classification and its applications to modern networks. Computer Networks 53(6), 759–776 (2009)

    Article  Google Scholar 

  10. Soysal, M., Schmidt, G.E.: Machine learning algorithms for accurate flow-based network traffic classification: Evaluation and comparison. Performance Evaluation 67(6), 451–467 (2010)

    Article  Google Scholar 

  11. Balamuralidhar, P., Prasad, R.: A context driven architecture for cognitive nodes. Wireless Personal Communications 45(1), 423–434 (2008)

    Article  Google Scholar 

  12. Erman, J., Arlitt, M., Mahanti, A.: Traffic classification using clustering algorithms. In: Proceedings of SIGCOMM Workshop on Mining Network Data, pp. 281–286 (2006)

    Google Scholar 

  13. Karagiannis, T., Papagiannaki, K., Faloutsos, M.: BLINC: multilevel traffic classification in the dark. In: Proceedings of the 2005 Conference on Applications, Technologies, Architectures, and Protocols for Computer Communications, pp. 229–240 (2005)

    Google Scholar 

  14. Constantinou, F., Mavrommantis, P.: Identifying known and unknown peer-to-peer traffic. In: IEE NCA 2006 Conference, pp. 93–102 (2006)

    Google Scholar 

  15. Nguyen, T., Armitage, G.: A Survey of Techniques for Internet Traffic Classification using Machine Learning. IEEE Communications Surveys and Tutorials 11(3), 37–52 (2008)

    Google Scholar 

  16. Pitchaimani, M., Ewy, B., Evans, J.: Evaluating Techniques for Network Layer Independence in Cognitive Networks. In: Proc. IEEE Int. Conf. on Commun. (ICC 2007), pp. 6527–6531 (2007)

    Google Scholar 

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© 2011 Springer-Verlag Berlin Heidelberg

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Gu, C., Zhang, S. (2011). Self-Adaptive QoS Control Mechanism in Cognitive Networks Based on Intelligent Service Awareness. In: Gong, Z., Luo, X., Chen, J., Lei, J., Wang, F.L. (eds) Web Information Systems and Mining. WISM 2011. Lecture Notes in Computer Science, vol 6987. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23971-7_51

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  • DOI: https://doi.org/10.1007/978-3-642-23971-7_51

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-23970-0

  • Online ISBN: 978-3-642-23971-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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