Optimization of Energy Efficient Network Migration Using Harmony Search

  • Stefan Türk
  • Rico Radeke
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6955)

Abstract

In this paper we describe the basic network migration problem in backbone networks to move from an existing to a new technology. Furthermore we use a generic harmony search algorithm to optimize the solution in terms of energy efficiency and costs. Harmony search is a probabilistic meta-heuristic which has been successfully adapted to many optimization problems. We analyze how harmony search can be used to calculate migration sequences with minimized energy consumption and financial costs. Results of parameter studies for the heuristic will be shown to evaluate the method. The achieved resource utilization to cover increasing network demands and the point of introduction within a certain time interval will be presented also.

Keywords

Internet Protocol Harmony Search Harmony Search Algorithm Harmony Memory Network Migration 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Cisco: Cisco visual networking index: Forecast and methology, 2008-2013, Tech. Rep. (2009)Google Scholar
  2. 2.
    Kiy, N.: Carrier-ethernet: Transportnetz für next generation networks. ntz 3-4, 28–29 (2009)Google Scholar
  3. 3.
    Michaelis, T., Duelli, M., Chamania, M., Lichtinger, B., Rambach, F., Türk, S.: Network planning, control and management perspectives on dynamic networking. In: 35th European Conference on Optical Communication, Vienna, Austria, p. 7.7.2 (2009)Google Scholar
  4. 4.
    Ciena, The value of otn for network convergence and ip/ethernet migration (2009), http://www.ciena.com/files/
  5. 5.
    Geem, Z.W., Kim, J.H., Loganathan, G.V.: A new heuristic optimization algorithm: Harmony search. Simulation 76(2), 60–68 (2001), http://sim.sagepub.com/content/76/2/60.abstract CrossRefGoogle Scholar
  6. 6.
    Dorigo, M., Birattari, M., Stutzle, T.: Ant colony optimization. IEEE Computational Intelligence Magazine 1(4), 28–39 (2006)CrossRefGoogle Scholar
  7. 7.
    Türk, S., Radeke, R., Lehnert, R.: Network migration using ant colony optimization. In: 9th Conference of Telecommunication, Media and Internet Techno-Economics (CTTE) (June 2010)Google Scholar
  8. 8.
    Türk, S., Sulaiman, S., Haidine, A., Lehnert, R., Michaelis, T.: Approaches for the migration of optical backbone networks towards carrier ethernet. In: IEEE Workshop on Enabling the Future Service-Oriented Internet - Towards Socially-Aware Networks, Honolulu, Hawaii, USA (2009)Google Scholar
  9. 9.
    Baliga, J., Ayre, R., Hinton, K., Sorin, W., Tucker, R.: Energy consumption in optical IP networks. Journal of Lightwave Technology 27(13), 2391–2403 (2009)CrossRefGoogle Scholar
  10. 10.
    Ferreiro, A.: Nobel 2 project: Migration guidelines with economic assessment and new business opportunities generated by NOBEL phase 2. Tech. Rep. (2008)Google Scholar
  11. 11.
    Palkopoulou, E., Schupke, D.A., Bauschert, T.: Energy efficiency and capex minimization for backbone network planning: is there a tradeoff? In: ANTS 2009: Proceedings of the 3rd International Conference on Advanced Networks and Telecommunication Systems, pp. 34–36. IEEE Press, USA (2009)Google Scholar
  12. 12.
    Idzikowski, F.: Power consumption of network elements in IP over WDM networks. TU Berlin, TKN Group, Tech. Rep. TKN-09-006 (2009)Google Scholar
  13. 13.
    Tamm, O.: Scaling and energy efficiency in next generation core networks and switches. In: ECOC, Vienna (2009)Google Scholar
  14. 14.
    Meusburger, C., Schupke, D., Eberspacher, J.: Multiperiod planning for optical networks-approaches based on cost optimization and limited budget. In: IEEE International Conference on Communications, ICC 2008, pp. 5390–5395 (2008)Google Scholar
  15. 15.
    Verbrugge, S.: Strategic planning of optical telecommunication networks in a dynamic and uncertain environment. Ph.D. dissertation, University of Ghent (2007)Google Scholar
  16. 16.
    Lee, K.S., Geem, Z.W.: A new structural optimization method based on the harmony search algorithm. Computers & Structures 82(9-10), 781–798 (2004), http://www.sciencedirect.com/science/article/B6V28-4BWMP8Y-2/2/cd0c48f22f516cc7df98796923cbe99a CrossRefGoogle Scholar
  17. 17.
    Mahdavi, M., Fesanghary, M., Damangir, E.: An improved harmony search algorithm for solving optimization problems. Applied Mathematics and Computation 188(2), 1567–1579 (2007), http://www.sciencedirect.com/science/article/B6TY8-4MM8B85-1/2/99776af451ff9cf34fec0d93a358aa6d MathSciNetCrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Stefan Türk
    • 1
  • Rico Radeke
    • 1
  1. 1.Dresden University of TechnologyDresdenGermany

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