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A memetic algorithm for the virtual network mapping problem

Author

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  • Johannes Inführ

    (Vienna University of Technology)

  • Günther Raidl

    (Vienna University of Technology)

Abstract

The Internet has ossified. It has lost its capability to adapt as requirements change. A promising technique to solve this problem is the introduction of network virtualization. Instead of directly using a single physical network, working just well enough for a limited range of applications, multiple virtual networks are embedded on demand into the physical network, each of them perfectly adapted to a specific application class. The challenge lies in mapping the different virtual networks with all the resources they require into the available physical network, which is the core of the virtual network mapping problem. In this work, we introduce a memetic algorithm that significantly outperforms the previously best algorithms for this problem. We also offer an analysis of the influence of different problem representations and in particular the implementation of a uniform crossover for the grouping genetic algorithm that may also be interesting outside of the virtual network mapping domain. Furthermore, we study the influence of different hybridization techniques and the behaviour of the developed algorithm in an online setting.

Suggested Citation

  • Johannes Inführ & Günther Raidl, 2016. "A memetic algorithm for the virtual network mapping problem," Journal of Heuristics, Springer, vol. 22(4), pages 475-505, August.
    • Handle: RePEc:spr:joheur:v:22:y:2016:i:4:d:10.1007_s10732-014-9274-x
    DOI: 10.1007/s10732-014-9274-x
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    References listed on IDEAS

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    1. Evelyn C. Brown & Cliff T. Ragsdale & Arthur E. Carter, 2007. "A Grouping Genetic Algorithm For The Multiple Traveling Salesperson Problem," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 6(02), pages 333-347.
    2. Hansen, Pierre & Mladenovic, Nenad, 2001. "Variable neighborhood search: Principles and applications," European Journal of Operational Research, Elsevier, vol. 130(3), pages 449-467, May.
    3. Pablo Moscato & Carlos Cotta, 2010. "A Modern Introduction to Memetic Algorithms," International Series in Operations Research & Management Science, in: Michel Gendreau & Jean-Yves Potvin (ed.), Handbook of Metaheuristics, chapter 0, pages 141-183, Springer.
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