Evolutionary Algorithms for Design of Virtual Private Networks

  • Igor KotenkoEmail author
  • Igor Saenko
Conference paper
Part of the Studies in Computational Intelligence book series (SCI, volume 798)


Virtual Private Networks (VPNs) is a most known technology to create the protected communication links via the Internet. The paper offers a new approach to solve the problem of VPN network design based on evolutionary algorithms (genetic and differential evolution). The joint accounting of network bandwidth, reliability and cost, which indices are calculated on the basis of the offered queuing theory models, is the feature of the considered problem. The experimental assessment of the suggested decisions shows that the evolutionary algorithms can improve the VPN network efficiency up to 40% in comparison with the standard variants of its creation. The comparative assessment of the suggested evolutionary algorithms shows higher convergence of the differential evolution algorithm.


Genetic algorithm Differential evolution Security Virtual Private Network Reliability 



This work was supported by grants of RFBR (projects No. 16-29-09482, 18-07-01369 and 18-07-01488), by the budget (the project No. AAAA-A16-116033110102-5), and by Government of Russian Federation (Grant 08-08).


  1. 1.
    Rehman, M.H.: Design and implementation of mobility for virtual private network users. Glob. J. Comput. Sci. Technol. Netw. Web Secur. 13(9), 34–39 (2013)Google Scholar
  2. 2.
    Yurcik, W., Doss, D.: A planning framework for implementing virtual private networks. IT Prof. 3, 41–44 (2001)CrossRefGoogle Scholar
  3. 3.
    Duffield, N.G., Goyal, P., Greenberg, A.: A flexible model for resource management in virtual private networks. SIGCOMM Comput. Commun. Rev. 29(4), 95–108 (1999)CrossRefGoogle Scholar
  4. 4.
    Duffield, N.G., Goyal, P., Greenberg, A., Mishra, P., Ramakrishnan, K.K., Van der Merwe, J.E.: Resource management with hoses: point-to-cloud services for virtual private networks. IEEE/ACM Trans. Netw. 10(5), 679–692 (2002)CrossRefGoogle Scholar
  5. 5.
    Raghunath, S., Ramakrishnan, K.K.: Resource management for virtual private networks. Commun. Mag. 45(4), 38–44 (2007)CrossRefGoogle Scholar
  6. 6.
    Eisenbrand, F., Grandoni, F., Oriolo, G., Skutella, M.: New approaches for virtual private network design. In: Automata, Languages and Programming. Lecture Notes in Computer Science, vol. 3580, pp. 1151–1162. Springer (2005)Google Scholar
  7. 7.
    Srikitja, A., Tipper, D.: QoS-based Virtual Private Network Design for an MPLS network. Accessed 10 Apr 2018
  8. 8.
    Italiano, G.F., Rastogi, R., Yener, B.: Restoration Algorithms for Virtual Private Networks in the Hose Model. Accessed 10 Apr 2018
  9. 9.
    Hurkens, C.A.J., Keijsper, J.C.M., Stougie, L.: Virtual private network design: a proof of the tree routing conjecture on ring networks. SIAM J. Discrete Math. 21(2), 482–503 (2004)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Altin, A., Amaldi, E., Belotti, P., Pinar, M.C.: Virtual private network design under traffic unce. Electron. Notes Discret. Math. 17, 19–22 (2004)CrossRefGoogle Scholar
  11. 11.
    Eisenbrand, F., Happ, E.: Provisioning a virtual private network under the presence of non-communicating groups. In: Algorithms and Complexity. Lecture Notes in Computer Science, vol. 3998, pp. 105–114. Springer (2006)Google Scholar
  12. 12.
    Haque, A., Ho, P.-H.: A study on the design of survivable optical virtual private networks (O-VPN). IEEE Trans. Reliab. 55(3), 516–524 (2006)CrossRefGoogle Scholar
  13. 13.
    Natarajan, M.C., Muthiah, R., Nachiappan, A.: Performance investigation of virtual private networks with different bandwidth allocations. IJCSI Int. J. Comput. Sci. 7(1), 58–63 (2010)Google Scholar
  14. 14.
    Secure Cloud Connectivity for Virtual Private Networks (white paper). Next-Generation Virtualized Managed Services for the Enterprise with Secure-on-Network Links to the Cloud, Juniper Networks, Inc. (2014)Google Scholar
  15. 15.
    Saenko, I., Kotenko, I.: A genetic approach for virtual computer network design. In: Intelligent Distributed Computing VIII. Studies in Computational Intelligence, vol. 570, pp. 95–105. Springer (2015)Google Scholar
  16. 16.
    Saenko, I., Kotenko, I.: Genetic algorithms for role mining problem. In: Proceeding of the 19th International Euromicro Conference on Parallel, Distributed and Network-based Processing, Ayia Napa, Cyprus, 9–11 February, pp. 646–650 (2011)Google Scholar
  17. 17.
    Saenko, I., Kotenko, I.: Design of virtual local area network scheme based on genetic optimization and visual analysis. J. Wirel. Mob. Netw. Ubiquitous Comput. Dependable Appl. (JoWUA) 5(4), 86–102 (2014)Google Scholar
  18. 18.
    Binitha, S., Sathya, S.S.: A survey of bio inspired optimization algorithms. Int. J. Soft Comput. Eng. (IJSCE) 2(2), 137–151 (2012)Google Scholar
  19. 19.
    Kotenko, I., Saenko, I.: The genetic approach for design of virtual private networks. In: Proceedings of the 2015 IEEE 18th International Conference on Computational Science and Engineering (CSE-2015), Porto, 21–23 October, pp. 168–175 (2015)Google Scholar
  20. 20.
    Kleinrock, L.: Queueing Systems. Wiley, New York (1975)zbMATHGoogle Scholar
  21. 21.
    Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)zbMATHGoogle Scholar
  22. 22.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization, and Machine Learning, 1st edn. Addison-Wesley, Boston (1989)zbMATHGoogle Scholar
  23. 23.
    Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Massachusetts (1998)zbMATHGoogle Scholar
  24. 24.
    Saenko, I., Kotenko, I.: Genetic optimization of access control schemes in virtual local area networks. In: Computer Network Security. Lecture Notes in Computer Science, vol. 6258, pp. 209–216. Springer (2010)Google Scholar
  25. 25.
    Storn, R., Price, K.: Differential evolution – a simple and efficient heuristic for global optimization over continuous spaces. J. Global Optim. 11, 341–359 (1997)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.St. Petersburg Institute for Informatics and Automation of the Russian Academy of SciencesSt. PetersburgRussia
  2. 2.ITMO UniversitySt. PetersburgRussia

Personalised recommendations