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Annals of Computer Science and Information Systems, Volume 13

Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems

The Algorithm for Sequential Analysis of Variants for Distribution of Virtual Machines in Data Center

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DOI: http://dx.doi.org/10.15439/2017F242

Citation: Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 13, pages 183187 ()

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Abstract. this work proposes an algorithm of sequential analysis of variants (SAV) to solve the distributional problem of allocation of virtual machines to physical servers in a data center. The set of tests and rules of the SAV algorithm is defined. The experimental results for problems of different dimensions are given. The comparison of the proposed algorithm with heuristic and genetic algorithms is accomplished. The time of finding solution required by the SAV algorithm depending on the dimension of the problem is evaluated. The recommendations for using the SAV algorithm are given. For tasks requiring high precision distribution it is better to use the SAV algorithm as it finds the optimal solution, whereas heuristic and evolutionary algorithms can quickly get an effective solution. The speed of the heuristic and evolutionary algorithms is not significantly dependent on the problem's size, but the quality of their solutions is worse than equivalent solution received with the SAV algorithm.


  1. J. H. Holland “Complex Adaptive Systems,” in Daedalus, vol. 121, no. 1, pp. 17–30; Winter 1992.
  2. K. S. Leung, Q. H. Duan, Z. B. Xu and C. K. Wong, “A new model of simulated evolutionary computation: convergence analysis and specifications,” IEEE Trans. on Evolutionary Computation, vol. 5, no. 1, pp. 3–16, 2001.
  3. S. Telenyk, E. Zharikov and O. Rolik, “Architecture and Conceptual Bases of Cloud IT Infrastructure Management,” in Advances in Intelligent Systems and Computing, Springer, 2017, pp. 41–62.
  4. S. Telenyk., O. Rolik, P.S. Savchenko and M. E. Bodaniuk, “Manageable genetic algorithm in tasks of distribution of virtual machines in data centres,” Visnyk of Cherkasy State Technological University), vol. 2, pp. 104–113, 2011.
  5. S. F. Telenik, A. I. Rolik, M. M. Bukasov and S. A. Androsov, “Genetic algorithms of decision of tasks of management resources and loading of centers of processing of data” Automatic. Automation. Electrical engineering complexes and systems, no. 1 (25), pp. 106–120, 2010.
  6. S. Singh and I. Chana, “A Survey on Resource Scheduling in Cloud Computing: Issues and Challenges,” Journal of Grid Computing, pp. 1–48, 2016.
  7. Z. Cao and S. Dong, “An energy-aware heuristic framework for virtual machine consolidation in cloud computing,” The Journal of Supercomputing, pp. 1–23, 2014.
  8. M. Sun, W. Gu, X. Zhang, H. Shi, and W. Zhang, “A matrix transformation algorithm for virtual machine placement in cloud,” in Trust, Security and Privacy in Computing and Communications (TrustCom), 2013 12th IEEE Int. Conf. on. IEEE, 2013, pp. 1778–1783.
  9. F. Lopez Pires and B. Baran, “Multi-objective virtual machine placement with service level agreement: A memetic algorithm approach,” in Proc. of the 2013 IEEE/ACM 6th Int. Conf. on Utility and Cloud Computing. IEEE Computer Society, 2013, pp. 203–210.
  10. W. Wang, H. Chen, and X. Chen, “An availability-aware virtual machine placement approach for dynamic scaling of cloud applications,” in Ubiquitous Intelligence & Computing and 9th Int. Conf. on Autonomic & Trusted Computing (UIC/ATC), 2012, pp. 509–516.
  11. V. S. Mikhalevich, “Consecutive optimization algorithms and their application. І. ІІ,” Cybernetics, no.1, pp. 45–55, no 2. pp. 85–88, 1965.
  12. E. N. Sipko, “The method for sequential analysis of variants to solve a scheduling problem,” Iskusstvenny intellekt, no. 1, pp. 243–250, 2011.