Advertisement

Self-management in Future Internet Wireless Networks: Dynamic Resource Allocation and Traffic Routing for Multi-service Provisioning

  • Ioannis P. Chochliouros
  • Nancy Alonistioti
  • Anastasia S. Spiliopoulou
  • George Agapiou
  • Andrej Mihailovic
  • Maria Belesioti
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 13)

Abstract

Evolution towards the Future (Internet) networks necessitates inclusion of self-management capabilities in modern network infrastructures, for a satisfactory provision of related services and for preserving network performance. We have considered a specific targeted methodology, in the form of the generic cognitive cycle model, which includes three distinct processes (i.e. Monitoring, Decision Making and Execution), known as the “MDE” model, able to support dynamic resource allocation and traffic routing schemes. For further understanding of the issue we have examined two essential use-cases of practical interest, both in the context of modern wireless infrastructures: The former was about dynamic spectrum re-allocation for efficient use of traffic, while the latter has examined intelligent dynamic traffic management for handling network overloads, to avoid congestion.

Keywords

Autonomic communications cognitive networks Future Internet generic cognitive cycle model self-configuration self-management self-organization spectrum re-allocation traffic routing WiMAX 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    The Netherlands Ministry of Economic Affairs: The Internet: A Shared Future (Publication Number 08ET13), The Hague (2008)Google Scholar
  2. 2.
    Commission of the European Communities: Communication on i2010 - A European Information Society for Growth and Development [COM(2005) 229 final, 01.06.2005], Brussels (2005)Google Scholar
  3. 3.
    DG Information Society and Media of the European Commission: A Compendium of European Projects on ICT Research Supported by the EU 7th Framework Programme for RTD, Brussels (2008)Google Scholar
  4. 4.
    Pfeifer, R., Scheier, C.: Understanding Intelligence. MIT Press, Cambridge (1999)Google Scholar
  5. 5.
    Elliott, C., Heile, B.: Self-Organizing, Self-Healing Wireless Networks. In: IEEE International Conference on Personal Wireless Communications, pp. 355–362 (2000)Google Scholar
  6. 6.
    Biskupski, B., Dowling, L., Sacha, J.: Properties and Mechanisms of Self-organizing MANET and P2P Systems. ACM Transactions on Autonomous and Adaptive Systems (TAAS) 2(1), 1–34 (2007)CrossRefGoogle Scholar
  7. 7.
    Blumenthal, M., Clark, D.D.: Rethinking the Design of the Internet: The End-to-End Arguments vs. the Brave New World. ACM Transactions on Internet Technology 1(1), 70–109 (2001)CrossRefGoogle Scholar
  8. 8.
    Weiss, T., Jondral, F.: Spectrum Pooling: An Innovative Strategy for the Enhancement of Spectrum Efficiency. IEEE Communications Magazine 42, S8–S14 (2004)CrossRefGoogle Scholar
  9. 9.
    Pioro, M., Mehdi, D.: Routing, Flow and Capacity Design in Communication and Computer Networks. Morgan Kaufmann, San Francisco (2004)zbMATHGoogle Scholar
  10. 10.
    Mitola, J.: Cognitive Radio. An Integrated Agent Architecture for Software Defined Radio. Ph.D. Thesis. Sweden (2000)Google Scholar
  11. 11.
    Thomas, R.W., DaSilva, L.A., MacKenzie, A.B.: Cognitive Networks. In: IEEE DySPAN 2005, November 2005, pp. 352–360 (2005)Google Scholar
  12. 12.
    Haykin, S.: Cognitive Radio: Brain-Empowered Wireless Communications. IEEE Journal on Selected Areas in Communications 23(2), 201–220 (2005)CrossRefGoogle Scholar
  13. 13.
    Buddhikot, M.M., Ryan, K.: Spectrum Management in Coordinated Dynamic Spectrum Access Based Cellular Networks. In: Proceedings of IEEE Dishpans 2005, November 2005, pp. 299–307 (2005)Google Scholar
  14. 14.
    Akyildiz, I.F., Lee, W., Vuran, M., Mohanty, S.: Next Generation/Dynamic Spectrum Access/Cognitive Radio Wireless Networks: A Survey. Computer Networks: The International Journal of Computer and Telecommunications Networking 50(13), 2127–2159 (2006)CrossRefzbMATHGoogle Scholar
  15. 15.
    Akyildiz, I.F., Altunbasak, Y., Fekri, F., Sivakumar, R.: AdaptNet: Adaptive Protocol Suite for Next Generation Wireless Internet. IEEE Communications Magazine 42(3), 128–138 (2004)CrossRefGoogle Scholar
  16. 16.
    Zander, J., Kim, S.-L., Almgren, M.: Radio Resource Management for Wireless Networks. Artech House (2001)Google Scholar
  17. 17.
    Buddhikot, M.M., Kolodzy, P., Miller, S., Ryan, K., Evans, J.: DIMSUMNet: New directions in wireless networking using coordinated dynamic spectrum access. In: Sixth IEEE International Symposium on World of Wireless Mobile and Multimedia Networks, June 2005, pp. 78–85 (2005)Google Scholar
  18. 18.
    Zhao, O., Sadler, B.: A Survey of Dynamic Spectrum Access. IEEE Signal Processing Magazine 79(3), 79–89 (2007)CrossRefGoogle Scholar
  19. 19.
    Ahuja, R.K., Magnanti, R.L., Orlin, J.B.: Network Flows. Prentice-Hall, Englewood Cliffs (1993)zbMATHGoogle Scholar
  20. 20.
    Ahlswede, R., Cai, N., Li, S.-Y.R., Yeung, R.W.: Network information flow. IEEE Trans. on Information Theory 46(4), 1204–1216 (2000)MathSciNetCrossRefzbMATHGoogle Scholar
  21. 21.
    Barford, P., Plonka, D.: Characteristics of Network Traffic Flow Anomalies. In: ACM SIGCOMM Internet Measurement Workshop, San Francisco, pp. 69–73 (2001)Google Scholar
  22. 22.
    Estfan, C., Varghese, G.: New Directions in Traffic Measurement and Accounting. In: ACM SIGCOMM Internet Measurement Workshop, San Francisco, pp. 75–80 (2001)Google Scholar
  23. 23.
    Fowler, H.J., Leland, W.E.: Local Area Network Traffic Characteristics, with Implications for Broadband Network Congestion Management. IEEE Journal of Selected Areas in Communications 9(7), 1139–1149 (1991)CrossRefGoogle Scholar
  24. 24.
    Sarachik, P., Panwar, S., Liang, P., Papavassiliou, S., Tsaih, D., Tassiulas, L.: A Modeling Approach for the Performance Management of High Speed Networks. In: Network Management and Control, vol. 2, pp. 149–163. Plenum Press, New York (1994)CrossRefGoogle Scholar
  25. 25.
    Whitehead, M.J., Williams, P.M.: Adaptive Network Overload Controls. BT Technology Journal 20(3), 31–54 (2002)CrossRefGoogle Scholar
  26. 26.
    Thomas, R.W., Friend, D.H., DaSilva, L.A., MacKenzie, A.B.: Cognitive Networks: Adaptation and Learning to Achieve End-to-End Performance Objectives. IEEE Communications Magazine 44(12), 51–57 (2006)CrossRefGoogle Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2009

Authors and Affiliations

  • Ioannis P. Chochliouros
    • 1
  • Nancy Alonistioti
    • 2
  • Anastasia S. Spiliopoulou
    • 3
  • George Agapiou
    • 1
  • Andrej Mihailovic
    • 4
  • Maria Belesioti
    • 1
  1. 1.Hellenic Telecommunications Organization (O.T.E.) S.A.Research Programs SectionAthensGreece
  2. 2.Dept. of Informatics and CommunicationsUniversity of AthensAthensGreece
  3. 3.General Directorate for Regulatory AffairsHellenic Telecommunications Organization (O.T.E.) S.A.AthensGreece
  4. 4.Centre for Telecommunications ResearchKing’s College London (KCL)LondonUK

Personalised recommendations