Energy-efficient VM-placement in cloud data center

Mishra, SK; Puthal, D; Sahoo, B; Jayaraman, PP; Jun, S; Zomaya, AY; Ranjan, R

Energy-efficient VM-placement in cloud data center

Mishra, SK Puthal, D

Sahoo, B Jayaraman, PP Jun, S Zomaya, AY Ranjan, R

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Publication Type:: Journal Article
Citation:: Sustainable Computing: Informatics and Systems, 2018, 20 pp. 48 - 55
Issue Date:: 2018-12-01

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Field	Value	Language
dc.contributor.author	Mishra, SK	en_US
dc.contributor.author	Puthal, D https://orcid.org/0000-0002-8332-278X	en_US
dc.contributor.author	Sahoo, B	en_US
dc.contributor.author	Jayaraman, PP	en_US
dc.contributor.author	Jun, S	en_US
dc.contributor.author	Zomaya, AY	en_US
dc.contributor.author	Ranjan, R	en_US
dc.date.issued	2018-12-01	en_US
dc.identifier.citation	Sustainable Computing: Informatics and Systems, 2018, 20 pp. 48 - 55	en_US
dc.identifier.issn	2210-5379	en_US
dc.identifier.uri	http://hdl.handle.net/10453/131779
dc.description.abstract	© 2018 Employing cloud computing to acquire the benefit of cloud by optimizing various parameters that meet changing demands is a challenging task. The optimal mapping of tasks to virtual machines (VMs) and VMs to physical machines (PMs) (known as VM placement) problem are necessary for advancing energy consumption and resource utilization. High heterogeneity of tasks as well as resources, great dynamism and virtualization make the consolidation issue more complicated in the cloud computing system. In this paper, a complete mapping (i.e., task VM and VM to PM) algorithm is proposed. The tasks are classified according to their resource requirement and then searching for the appropriate VM and again searching for the appropriate PM where the selected VM can be deployed. The proposed algorithm reduces the energy consumption by depreciating the number of active PMs, while also minimizes the makespan and task rejection rate. We have evaluated our proposed approach in CloudSim simulator, and the results demonstrate the effectiveness of the proposed algorithm over some existing standard algorithms.	en_US
dc.relation.ispartof	Sustainable Computing: Informatics and Systems	en_US
dc.relation.isbasedon	10.1016/j.suscom.2018.01.002	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Energy-efficient VM-placement in cloud data center	en_US
dc.type	Journal Article
utslib.citation.volume	20	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Information, Systems and Modelling
utslib.copyright.status	closed_access	*
pubs.publication-status	Published	en_US
pubs.volume	20	en_US

Abstract:

© 2018 Employing cloud computing to acquire the benefit of cloud by optimizing various parameters that meet changing demands is a challenging task. The optimal mapping of tasks to virtual machines (VMs) and VMs to physical machines (PMs) (known as VM placement) problem are necessary for advancing energy consumption and resource utilization. High heterogeneity of tasks as well as resources, great dynamism and virtualization make the consolidation issue more complicated in the cloud computing system. In this paper, a complete mapping (i.e., task VM and VM to PM) algorithm is proposed. The tasks are classified according to their resource requirement and then searching for the appropriate VM and again searching for the appropriate PM where the selected VM can be deployed. The proposed algorithm reduces the energy consumption by depreciating the number of active PMs, while also minimizes the makespan and task rejection rate. We have evaluated our proposed approach in CloudSim simulator, and the results demonstrate the effectiveness of the proposed algorithm over some existing standard algorithms.

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http://hdl.handle.net/10453/131779