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The algorithm for sequential analysis of variants for distribution of virtual machines in data center

Wybrane pełne teksty z tego czasopisma
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Warianty tytułu
Konferencja
Communication Papers of the 2017 Federated Conference on Computer Science and Information Systems
Języki publikacji
EN
Abstrakty
EN
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.
Rocznik
Tom
Strony
183--187
Opis fizyczny
Bibliogr. 12 poz., rys., wykr.
Twórcy
  • Department of Automation and Control in Technical Systems, National Technical University of Ukraine, “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv, Ukraine
  • Department of Automation and Control in Technical Systems, National Technical University of Ukraine, “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv, Ukraine
  • Department of Automation and Control in Technical Systems, National Technical University of Ukraine, “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv, Ukraine
  • Department of Automation and Information Technologies, Faculty of Electrical and Computer Engineering, Cracow University of Technology, Cracow, Poland
  • Lviv State University of Life Safety, Lviv, Ukraine
Bibliografia
  • 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.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-93f253aa-91a2-4367-ae01-243a3b575e3a
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