Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

A model for virtual network embedding across multiple infrastructure providers using genetic algorithm


Network virtualization is an important aspect in cloud computing, where network is assumed to be consisting of infinite amount of nodes and links. The substrate network (physical network), has limited capacity and so virtual network embedding on the substrate network becomes a problem. Virtual network embedding is a computationally hard problem, considering various constraints on nodes and links. The proposed work applies Genetic Algorithm for the virtual network embedding problem for mapping multiple virtual network requests on infrastructure providers managing multiple substrate networks. Performance evaluation, through simulations, indicates that the proposed model preforms better for the performance metrics such as infrastructure provider revenue, acceptance ratio and node and link utilization in comparison to few other contemporary mapping models.

This is a preview of subscription content, log in to check access.


  1. 1

    Anderson T, Peterson L, Shenker S, et al. Overcoming the Internet impasse through virtualization. Computer, 2005, 38: 34–41

  2. 2

    Zhu Y, Ammar M H. Algorithms for assigning substrate network resources to virtual network components. In: Proceedings of the 25th IEEE International Conference on Computer Communications, Barcelona, 2006. 1–12

  3. 3

    Lischka J, Karl H. A virtual network mapping algorithm based on subgraph isomorphism detection. In: Proceedings of the 1st ACM Workshop on Virtualized Infrastructure Systems and Architectures, Barcelona, 2009. 81–88

  4. 4

    Lu J, Turner J. Efficient mapping of virtual networks onto a shared substrate. Technical Report WUCSE-2006-35. Washington University, 2006

  5. 5

    Houidi I, Louati W, Zeghlache D. A distributed virtual network mapping algorithm. In: Proceedings of IEEE International Conference on Communications, Beijing, 2008. 5634–5640

  6. 6

    Yu M, Yi Y, Rexford J, et al. Rethinking virtual network embedding: substrate support for path splitting and migration. ACM SIGCOMM Comput Commun Rev, 2008, 38: 17–29

  7. 7

    Chowdhury N M M K, Rahman M R, Boutaba R. Virtual network embedding with coordinated node and link mapping. In: Proceedings of the 28th IEEE International Conference on Computer Communications, Rio de Janeiro, 2009. 783–791

  8. 8

    Melo M, Sargento S, Killat U, et al. Optimal virtual network embedding: Node-link formulation. IEEE Trans Netw Serv Manag, 2013, 10: 356–368

  9. 9

    Butt N F, Chowdhury M, Boutaba R. Topology-awareness and reoptimization mechanism for virtual network embedding. In: Proceedings of the 9th International IFIP TC 6 Networking Conference, Chennai, 2010. 27–39

  10. 10

    Nogueira J, Melo M, Carapinha J, et al. Virtual network mapping into heterogeneous substrate networks. In: Proceedings of IEEE Symposium on Computers and Communications (ISCC). IEEE: Washington, DC, 2011. 438–444

  11. 11

    Dorigo M, Caro G D, Gambardella L M. Ant algorithms for discrete optimization. Artif Life, 1999, 5: 137–172

  12. 12

    Kennedy J. Particle swarm optimization. In: Sammut C, Webb G I, eds. Encyclopedia of Machine Learning. New York: Springer US, 2010. 760–766

  13. 13

    Chu S-C, Tsai P-W. Computational intelligence based on the behavior of cats. Int J Innov Comput Inform Control, 2007, 3: 163–173

  14. 14

    Golberg D E. Genetic Algorithms in Search, Optimization, and Machine Learning. Boston: Addison-Wesley Longman Publishing Co., Inc., 1989

  15. 15

    Zhang Z B, Cheng X, Su S, et al. A unified enhanced particle swarm optimization-based virtual network embedding algorithm. Int J Commun Syst, 2013, 26: 1054–1073

  16. 16

    Fajjari I, Aitsaadi N, Pujolle G, et al. VNE-AC: virtual network embedding algorithm based on ant colony metaheuristic. In: Proceedings of IEEE International Conference on Communications (ICC), Kyoto, 2011. 1–6

  17. 17

    Mi X M, Chang X L, Liu J Q, et al. Embedding virtual infrastructure based on genetic algorithm. In: Proceedings of the 13th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Beijing, 2012. 239–244

  18. 18

    Sivanandam S N, Deepa S N. Introduction to Genetic Algorithms. Berlin/Heidelberg: Springer-Verlag, 2007

  19. 19

    Spears W M, Jong K A D. An analysis of multi-point crossover. Technical Report, DTIC Document, 1990

  20. 20

    Zhang S, Qian Z Z, Wu J, et al. An opportunistic resource sharing and topology-aware mapping framework for virtual networks. In: Proceedings of the 31st Annual IEEE International Conference on Computer Communications, Orlando, 2012. 2408–2416

  21. 21

    Rahman M R, Boutaba R. SVNE: survivable virtual network embedding algorithms for network virtualization. IEEE Trans Netw Serv Manag, 2013, 10: 105–118

  22. 22

    Chowdhury M, Rahman M R, Boutaba R. Vineyard: virtual network embedding algorithms with coordinated node and link mapping. IEEE/ACM Trans Netw, 2012, 20: 206–219

  23. 23

    Hasan M M, Amarasinghe H, Karmouch A. Network virtualization: dealing with multiple infrastructure providers. In: Proceedings of IEEE International Conference on Communications (ICC), Ottawa, 2012. 5890–5895

  24. 24

    Houidi I, Louati W, Ameur W B, et al. Virtual network provisioning across multiple substrate networks. Comput Netw, 2011, 55: 1011–1023

Download references


This work was supported by UGC-UPEII, New Delhi. Also, authors accord their sincere thanks to the anonymous reviewers for the useful suggestions.

Author information

Correspondence to Deo Prakash Vidyarthi.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Pathak, I., Vidyarthi, D.P. A model for virtual network embedding across multiple infrastructure providers using genetic algorithm. Sci. China Inf. Sci. 60, 040308 (2017).

Download citation


  • network virtualization
  • virtual network embedding
  • substrate network
  • NP-hard
  • genetic algorithm