Task Based System Load Balancing Approach in Cloud Environments

Ramezani, F; Lu, J; Hussain, F

Task Based System Load Balancing Approach in Cloud Environments

Ramezani, F

Lu, J

Hussain, F

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Publication Type:: Conference Proceeding
Citation:: Advances in Intelligent Systems and Computing, 2014, 214 pp. 31 - 42
Issue Date:: 2014-01-01

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Field	Value	Language
dc.contributor.author	Ramezani, F https://orcid.org/0000-0002-0368-321X	en_US
dc.contributor.author	Lu, J https://orcid.org/0000-0003-0690-4732	en_US
dc.contributor.author	Hussain, F https://orcid.org/0000-0003-1513-8072	en_US
dc.date.issued	2014-01-01	en_US
dc.identifier.citation	Advances in Intelligent Systems and Computing, 2014, 214 pp. 31 - 42	en_US
dc.identifier.issn	2194-5357	en_US
dc.identifier.uri	http://hdl.handle.net/10453/32979
dc.description.abstract	Live virtual machine (VM) migration is a technique for transferring an active VM from one physical host to another without disrupting the VM. This technique has been proposed to reduce the downtime for migrated overload VMs. As VMs migration takes much more times and cost in comparison with tasks migration, this study develops a novel approach to confront with the problem of overload VM and achieving system load balancing, by assigning the arrival task to another similar VM in a cloud environment. In addition, we propose a multi-objective optimization model to migrate these tasks to a new VM host applying multi-objective genetic algorithm (MOGA). In the proposed approach, there is no need to pause VM during migration time. In addition, as contrast to tasks migration, VM live migration takes longer to complete and needs more idle capacity in host physical machine (PM), the proposed approach will significantly reduce time, downtime memory, and cost consumption. © Springer-Verlag Berlin Heidelberg 2014.	en_US
dc.relation.ispartof	Advances in Intelligent Systems and Computing	en_US
dc.relation.isbasedon	10.1007/978-3-642-37832-4_4	en_US
dc.title	Task Based System Load Balancing Approach in Cloud Environments	en_US
dc.type	Conference Proceeding
utslib.citation.volume	214	en_US
utslib.for	0806 Information Systems	en_US
dc.location.activity	Beijing, China
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 Computer Science
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	214	en_US

Abstract:

Live virtual machine (VM) migration is a technique for transferring an active VM from one physical host to another without disrupting the VM. This technique has been proposed to reduce the downtime for migrated overload VMs. As VMs migration takes much more times and cost in comparison with tasks migration, this study develops a novel approach to confront with the problem of overload VM and achieving system load balancing, by assigning the arrival task to another similar VM in a cloud environment. In addition, we propose a multi-objective optimization model to migrate these tasks to a new VM host applying multi-objective genetic algorithm (MOGA). In the proposed approach, there is no need to pause VM during migration time. In addition, as contrast to tasks migration, VM live migration takes longer to complete and needs more idle capacity in host physical machine (PM), the proposed approach will significantly reduce time, downtime memory, and cost consumption. © Springer-Verlag Berlin Heidelberg 2014.

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http://hdl.handle.net/10453/32979