Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers

SUMMARY The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large‐scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in hi...

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Bibliographic Details
Published in:Concurrency and computation Vol. 24; no. 13; pp. 1397 - 1420
Main Authors: Beloglazov, Anton, Buyya, Rajkumar
Format: Journal Article
Language:English
Published: Chichester, UK John Wiley & Sons, Ltd 10.09.2012
Subjects:
Cloud computing
dynamic consolidation
Green IT
resource management
virtualization
ISSN:1532-0626, 1532-0634
Online Access:Get full text
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Summary:SUMMARY The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large‐scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allows Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy‐performance trade‐off, as aggressive consolidation may lead to performance degradation. Because of the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct a competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the service level agreement. We validate the high efficiency of the proposed algorithms by extensive simulations using real‐world workload traces from more than a thousand PlanetLab VMs. Copyright © 2011 John Wiley & Sons, Ltd.
Bibliography:ArticleID:CPE1867
istex:8B0F017E256A98B5B2773D26533CCFBECD738BFE
ark:/67375/WNG-XD9JMP54-9
ISSN:1532-0626
1532-0634
DOI:10.1002/cpe.1867