Exact algorithms for energy-efficient virtual machine placement in data centers

Journal article

Wei, Chen, Hu, Zhi-Hua and Wang, You-Gan. (2020). Exact algorithms for energy-efficient virtual machine placement in data centers. Future Generation Computer Systems. 106, pp. 77-91. https://doi.org/10.1016/j.future.2019.12.043
AuthorsWei, Chen, Hu, Zhi-Hua and Wang, You-Gan
Abstract

Virtual machine placement (VMP) and power management are essential topics in the development of cloud computing and data centers. The assignment of a virtual machine to physical machine impacts the energy consumption, the makespan, and the idle time of physical machines. In this paper, we formulate the problem as a three-dimension bin-packing optimization to minimize the energy cost of working machines and idle machines. By considering the CPU and memory requirements from a virtual machine, the assignment is constrained under the capacities of the physical machine. Inspired by the best-fit decreasing algorithm, four variants of this exact algorithm are developed to address the multiple-objective problem under multiple-capacity constraints. Experimental results demonstrate the effectiveness of the proposed algorithms on small-, medium- and large-scale instances profiled from data centers. The results indicate that the algorithms assigning virtual machines to the physical machines of best-fit hosting time is competitive in cases with loose capacity constraints, and the energy-efficiency best-fit algorithm produces efficient assignments when a makespan limit is required on the physical machines. The algorithm combining the fit rules has a linear computing time concerning the numbers of physical and virtual machines, and a stable performance that obtains gaps of results lower than 5.8% compared to an on-the-shelf mixed-integer linear program solver.

KeywordsVirtual machine placement; Data center; Computational service supply chain; Bin packing problem; First-fit algorithm
Year01 Jan 2020
JournalFuture Generation Computer Systems
Journal citation106, pp. 77-91
PublisherElsevier B.V.
ISSN0167-739X
Digital Object Identifier (DOI)https://doi.org/10.1016/j.future.2019.12.043
Web address (URL)https://www.sciencedirect.com/science/article/pii/S0167739X19319594
Open accessPublished as non-open access
Research or scholarlyResearch
Page range77-91
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Print08 Jan 2020
Publication process dates
Accepted27 Dec 2019
Deposited13 Jan 2023
Additional information

© 2020 Elsevier B.V. All rights reserved.

Place of publicationNetherlands
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