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The Journal of Supercomputing

, Volume 75, Issue 12, pp 8059–8093 | Cite as

A maximally robustness embedding algorithm in virtual data centers with multi-attribute node ranking based on TOPSIS

  • L. Shooshtarian
  • F. SafaeiEmail author
Article
  • 52 Downloads

Abstract

The virtualization of the data center network is one of the technologies that enable the performance guarantee and more flexibility and improve the utilization of infrastructure resources in cloud computing. One of the key issues in the management of virtual data center (VDC) is VDC embedding, which deals with the efficient mapping of required virtual network resources from the shared resources of the infrastructure provider (InP). In this paper, we propose a new VDC embedding algorithm that is different from previous works in many aspects. First, the provision of robustness for data center infrastructure is one of the critical requirements of cloud technology; however, this challenge has not been considered in the related literature. In order to analyze and evaluate the robustness of the infrastructure network, the classical and spectral graph robustness metrics are employed. Second, in order to avoid imbalance mapping and increase the efficiency of infrastructure resources, besides the resource dynamic capacity, four node attributes are exploited to compute the nodes mapping potential. The TOPSIS technique for nodes ranking has been used to increase the compatibility with the ideal solution. Third, unlike previous works in which the mapping phases of nodes and links are getting used to being separated, in the proposed algorithm, the virtual network is mapped to a physical network in a single step. Fourth, we also consider resources for network nodes (switches or routers). For these purposes, a multi-objective mathematical optimization problem is extracted with two goals of maximizing infrastructure network robustness and minimizing the long-term average cost-to-revenue ratio mapping for InPs. Finally, a new single-stage (non-dominated sorting-based genetic algorithm) NSGAII-based online VDCE algorithm is presented, where node mapping is TOP-MANR based and edge mapping is based on the shortest path. The fat-tree topology is considered for the substrate and virtual networks, and these two networks are modeled as a weighted undirected graph.

Keywords

Virtual network embedding algorithm (VNE) Virtual data center network (VDC) Network robustness Data center network virtualization TOPSIS NSGAII Optimization 

Notes

References

  1. 1.
    Zhang Q, Zhani MF, Jabri M, Boutaba R (2014) Venice: reliable virtual data center embedding in clouds. In: IEEE INFOCOM 2014—IEEE Conference on Computer Communications, Toronto, ON, pp 289–297Google Scholar
  2. 2.
    Gilesh MP, Kumar SDM, Jacob L, Bellur U (2017) Towards a complete virtual data center embedding algorithm using hybrid strategy. In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), Atlanta, GA, pp 2616–2617Google Scholar
  3. 3.
    Downtime Outages and failures-understanding their true costs. https://www.evolven.com/blog/downtime-outages-and-failures-understanding-their-true-costs.html. Accessed July 2019
  4. 4.
    Tizghadam A, Leon-Garcia A (2008) On robust traffic engineering in core networks. In: IEEE GLOBECOM, December 2008Google Scholar
  5. 5.
    Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  6. 6.
    Rost M, Schmid S (2018) NP-completeness and inapproximability of the virtual network embedding problem and its variants. arXiv:1801.03162 [CoRR]
  7. 7.
    Zheng X, Tian J, Xiao X et al (2018) A heuristic survivable virtual network mapping algorithm. Soft Comput 23(5):1453–1463CrossRefGoogle Scholar
  8. 8.
    Zhani MF, Zhang Q, Simona G, Boutaba R (2013) VDC planner: dynamic migration-aware virtual data center embedding for clouds. In: 2013 IFIP/IEEE International Symposium on Integrated Network Management (IM 2013), Ghent, pp 18–25Google Scholar
  9. 9.
    Bianchi F, Presti FL (2017) A Markov reward based resource-latency aware heuristic for the virtual network embedding problem. ACM SIGMETRICS Perform Eval Rev 44(4):57–68CrossRefGoogle Scholar
  10. 10.
    Amokrane A, Zhani MF, Langar R, Boutaba R, Pujolle G (2013) Greenhead: virtual data center embedding across distributed infrastructures. IEEE Trans Cloud Comput 1(1):36–49CrossRefGoogle Scholar
  11. 11.
    Wang T, Qin B, Hamdi M (2015) An efficient framework for online virtual network embedding in virtualized cloud data centers. In: 2015 IEEE 4th International Conference on Cloud Networking (CloudNet), Niagara Falls, ON, pp 159–164Google Scholar
  12. 12.
    Bhamare D, Samaka M, Erbad A, Jain R, Gupta L, Chan HA (2017) Optimal virtual network function placement in multi-cloud service function chaining architecture. Comput Commun 102:1–16CrossRefGoogle Scholar
  13. 13.
    Tavakoli-Someh S, Rezvani MH (2019) Multi-objective virtual network function placement using NSGA-II meta-heuristic approach. J Supercomput.  https://doi.org/10.1007/s11227-019-02849-y CrossRefGoogle Scholar
  14. 14.
    Yi B, Wang X, Huang M (2017) Design and evaluation of schemes for provisioning service function chain with function scalability. J Netw Comput Appl 93(1):197–214.  https://doi.org/10.1016/j.jnca.2017.05.013 CrossRefGoogle Scholar
  15. 15.
    Kar B, Wu EH-K, Lin Y-D (2018) Energy cost optimization in dynamic placement of virtualized network function chains. IEEE Trans Netw Serv Manag 15(1):372386CrossRefGoogle Scholar
  16. 16.
    Bodík P, Menache I, Chowdhury M, Mani P, Maltz DA, Stoica I (2012) Surviving failures in bandwidth-constrained datacenters. In: Proceedings of Conference on Applications, Technologies, Architecture, and Protocols for Computer Communication, pp 431–442Google Scholar
  17. 17.
    Herker S, Khan A, An X (2013) Survey on survivable virtual network embedding problem and solutions. In: The Ninth International Conference on Networking and Services, pp 99–104Google Scholar
  18. 18.
    Vishwanath KV, Nagappan N (2010) Characterizing cloud computing hardware reliability. In: Proceedings of ACM Symposium on Cloud Computing (SoCC)Google Scholar
  19. 19.
    Correa ES, Fletscher LA, Botero JF (2015) Virtual data center embedding: a survey. IEEE Latin Am Trans 13(5):1661–1670CrossRefGoogle Scholar
  20. 20.
    Greenberg A et al (2009) The cost of a cloud: research problems in data center networks. ACM SIGCOMM Comput Commun Rev 39(1):68–79MathSciNetCrossRefGoogle Scholar
  21. 21.
    Velocity and the bottom line. http://radar.oreilly.com/2009/07/velocity-making-your-site-fast.html. Accessed 17 Aug 2017
  22. 22.
    Newman MEJ (2005) A measure of betweenness centrality based on random walks. Social Networks 27(1):39–54CrossRefGoogle Scholar
  23. 23.
    Bigdeli A, Tizghadam A, Garcia AL (2009) Comparison of network criticality, algebraic connectivity, and other graphmetrics. In: Proceedings of the 1st Annual Workshop on Simplifying Complex Network for PractitionersGoogle Scholar
  24. 24.
    Gong S, Chen J, Zhao S, Zhu Q (2016) Virtual network embedding with multi-attribute node ranking based on TOPSIS. KSII Trans Internet Inf Syst 10(2):522–541Google Scholar
  25. 25.
    Mi X, Chang X, Liu J, Sun L, Xing B (2012) Embedding virtual infrastructure based on genetic algorithm. In: 2012 13th International Conference on Parallel and Distributed Computing, Applications and Technologies, Beijing, pp 239–244Google Scholar
  26. 26.
    Zhou Z, Chang X, Yang Y, Li L (2016) Resource-aware virtual network parallel embedding based on genetic algorithm. In: 2016 17th International Conference on Parallel and Distributed Computing, Applications and Technologies (PDCAT), Guangzhou, pp 81–86Google Scholar
  27. 27.
    Yu M, Yi Y, Rexford J, Chiang M (2008) Rethinking virtual network embedding: substrate support for path splitting and migration. ACM SIGCOMM Comput Commun Rev 38(2):17–29CrossRefGoogle Scholar
  28. 28.
    Freeman LC (1977) A set of measures of centrality based upon betweenness. Sociometry 40(1):35–41CrossRefGoogle Scholar
  29. 29.
    Borgatti SP (2005) Centrality and network flow. Soc Netw 27(1):55–71MathSciNetCrossRefGoogle Scholar
  30. 30.
    Hwang CL, Yoon KP (1981) Multiple attribute decision making: methods and applications. Springer, BerlinCrossRefGoogle Scholar
  31. 31.
    Roszkowska E (2011) “Multi-criteria decision making models by applying the TOPSIS method to crisp and interval data. Mult Criteria Decis Mak Univ Econ Katow 6:200–230Google Scholar
  32. 32.
    Fiedler M (1973) Algebraic connectivity of graphs. Czechoslov Math J 23(2):298–305MathSciNetzbMATHGoogle Scholar
  33. 33.
    Jamakovic A, Uhlig S (2007) Influence of the network structure on robustness. In: 2007 15th IEEE International Conference on Networks, Adelaide, SA, pp 278–283Google Scholar
  34. 34.
    Wei P, Sun D (2011) Weighted algebraic connectivity: an application to airport transportation network. In: 18th IFAC World Congress, Milan, Italy. IFAC, Aug–Sep 2011CrossRefGoogle Scholar
  35. 35.
    Mohar B, Alavi Y, Chartrand G, Oellermann OR (1991) The Laplacian spectrum of graphs. Graph Theory Comb Appl 2:871–898MathSciNetGoogle Scholar
  36. 36.
    Katoh N, Ibaraki T, Mine H (1982) An efficient algorithm for k-shortest simple-paths. Networks 12:411–427MathSciNetCrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Faculty of Computer Science and EngineeringShahid Beheshti University G.C.Evin, TehranIran

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