Multi-objective Optimization Framework for VMI Distribution in Federated Cloud Repositories

  • Dragi Kimovski
  • Nishant Saurabh
  • Sandi Gec
  • Vlado Stankovski
  • Radu Prodan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10104)

Abstract

Cloud Federation facilitates the concept of aggregation of multiple services administered by different providers, thus opening the possibility for the customers to profit from lower cost and better performance, while allowing for the cloud providers to offer more sophisticated services. Unfortunately, current state-of-the-art does not provide any substantial means for streamlined adaptation of federated Cloud environments. One of the essential barriers that prevents Cloud federation is the inefficient management of distributed storage repositories for Virtual Machine Images (VMI). In such environments, the VMIs are currently stored by Cloud providers in proprietary centralised repositories without considering application characteristics and their runtime requirements, causing high deployment and instantiation overheads. In this paper, a novel multi-objective optimization framework for VMI placement across distributed repositories in federated Cloud environment has been proposed. Based on the communication performance requirements, VMI use patterns, and structure of images or input data, the framework provides efficient means for transparent optimization of the distribution and placement of VMIs across distributed repositories to significantly lower their provisioning time for complex resource requests and for executing the user applications.

Keywords

Federated Cloud environment Distributed storage repositories Multi-objective optimization 

References

  1. 1.
    Goiri, I., Guitart, J., Torres, J.: Characterizing cloud federation for enhancing providers’ profit. In: 2010 IEEE 3rd International Conference on Cloud Computing (CLOUD), pp. 123–130. IEEE, July 2010Google Scholar
  2. 2.
    Villegas, D., Bobroff, N., Rodero, I., Delgado, J., Liu, Y., Devarakonda, A., Parashar, M.: Cloud federation in a layered service model. J. Comput. Syst. Sci. 78(5), 1330–1344 (2012)CrossRefGoogle Scholar
  3. 3.
    Durillo, J.J., Nebro, A.J.: jMetal: a Java framework for multi-objective optimization. Adv. Eng. Softw. 42(10), 760–771 (2011)CrossRefGoogle Scholar
  4. 4.
    Branke, J., et al. (eds.): Multiobjective Optimization: Interactive and Evolutionary Approaches, vol. 5252. Springer, Heidelberg (2008)MATHGoogle Scholar
  5. 5.
    Kurze, T., Klems, M., Bermbach, D., Lenk, A., Tai, S., Kunze, M.: Cloud federation. Cloud Comput. 2011, 32–38 (2011)Google Scholar
  6. 6.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.A.M.T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  7. 7.
    Feng, W.C., Balaji, P., Baron, C., Bhuyan, L.N., Panda, D.K.: Performance characterization of a 10-Gigabit Ethernet TOE. In: 2005 of Proceedings 13th Symposium on High Performance Interconnects, pp. 58–63. IEEE, August 2005Google Scholar
  8. 8.
    Abburu, S.: A survey on ontology reasoners and comparison. Int. J. Comput. Appl. 57(17) (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Dragi Kimovski
    • 1
  • Nishant Saurabh
    • 1
  • Sandi Gec
    • 2
  • Vlado Stankovski
    • 2
  • Radu Prodan
    • 1
  1. 1.Distributed and Parallel Systems, Institute of InformaticsUniversity of InnsbruckInnsbruckAustria
  2. 2.Faculty of Civil and Geodetic Engineering and Faculty of Computer and Information ScienceUniversity of LjubljanaLjubljanaSlovenia

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