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Efficient Virtual Machine Placement Algorithms for Consolidation in Cloud Data Centers

Loiy Alsbatin^{1, 2}, Gürcü Öz¹ and Ali Hakan Ulusoy³

1. Department of Computer Engineering, Faculty of Engineering, Eastern Mediterranean University
   Famagusta, North Cyprus via Mersin 10 Turkey
   loiy.alsbatin@gmail.com, gurcu.oz@emu.edu.tr
2. Department of Computer Science, Collage of Computing and Information Technology, Shaqra University
   Riyadh, Saudi Arabia
3. Department of Information Technology, School of Computing and Technology, Eastern Mediterranean University
   Famagusta, North Cyprus via Mersin 10 Turkey
   alihakan.ulusoy@emu.edu.tr

Abstract

Dynamic Virtual Machine (VM) consolidation is a successful approach to improve the energy efficiency and the resource utilization in cloud environments. Consequently, optimizing the online energy-performance tradeoff directly influences quality of service. In this study, algorithms named as CPU Priority based Best-Fit Decreasing (CPBFD) and Dynamic CPU Priority based Best-Fit Decreasing (DCPBFD) are proposed for VM placement. A number of VM placement algorithms are implemented and compared with the proposed algorithms. The algorithms are evaluated through simulations with real-world workload traces and it is shown that the proposed algorithms outperform the known algorithms. The simulation results clearly show that CPBFD and DCPBFD provide the least service level agreement violations, least VM migrations, and efficient energy consumption.

Key words

Cloud computing, energy consumption, dynamic consolidation, virtualization

Digital Object Identifier (DOI)

https://doi.org/10.2298/CSIS

Publication information

Volume 17, Issue 1 (January 2020)
Year of Publication: 2020
ISSN: 2406-1018 (Online)
Publisher: ComSIS Consortium

Full text

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How to cite

Alsbatin, L., Öz, G., Ulusoy, A. H.: Efficient Virtual Machine Placement Algorithms for Consolidation in Cloud Data Centers. Computer Science and Information Systems, Vol. 17, No. 1, 29-50. (2020), https://doi.org/10.2298/CSIS