Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty

Autores
Chamas, Nabil; López-Pires, Fabio; Barán, Benjamín
Año de publicación
2017
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
Cloud computing providers must support requests for resources in dynamic environments, considering service elasticity and overbooking of physical resources. Due to the randomness of requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. In this context, a renewed formulation of the VMP problem is presented, considering the optimization of four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. To solve the presented formulation, a two-phase optimization scheme is considered, composed by an online incremental VMP phase (iVMP) and an offline VMP reconfiguration (VMPr) phase. An experimental evaluation of five algorithms taking into account 400 different scenarios was performed, considering three VMPr Triggering and two VMPr Recovering methods as well as three VMPr resolution alternatives. Experimental results indicate which algorithm outperformed the other evaluated algorithms, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
Materia
Ciencias Informáticas
virtual machine placement
cloud computing
overbooking
elasticity
uncertainty
incremental vmp
vmp reconfiguration
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/65519
Acceder
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network_name_str SEDICI (UNLP)
spelling Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under UncertaintyChamas, NabilLópez-Pires, FabioBarán, BenjamínCiencias Informáticasvirtual machine placementcloud computingoverbookingelasticityuncertaintyincremental vmpvmp reconfigurationCloud computing providers must support requests for resources in dynamic environments, considering service elasticity and overbooking of physical resources. Due to the randomness of requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. In this context, a renewed formulation of the VMP problem is presented, considering the optimization of four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. To solve the presented formulation, a two-phase optimization scheme is considered, composed by an online incremental VMP phase (iVMP) and an offline VMP reconfiguration (VMPr) phase. An experimental evaluation of five algorithms taking into account 400 different scenarios was performed, considering three VMPr Triggering and two VMPr Recovering methods as well as three VMPr resolution alternatives. Experimental results indicate which algorithm outperformed the other evaluated algorithms, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs.Sociedad Argentina de Informática e Investigación Operativa (SADIO)2017-09info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdfhttp://sedici.unlp.edu.ar/handle/10915/65519enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-sa/4.0/Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:09:38Zoai:sedici.unlp.edu.ar:10915/65519Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:09:38.648SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty
title Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty
spellingShingle Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty
Chamas, Nabil
Ciencias Informáticas
virtual machine placement
cloud computing
overbooking
elasticity
uncertainty
incremental vmp
vmp reconfiguration
title_short Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty
title_full Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty
title_fullStr Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty
title_full_unstemmed Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty
title_sort Two-Phase Virtual Machine Placement Algorithms for Cloud Computing: An Experimental Evaluation under Uncertainty
dc.creator.none.fl_str_mv Chamas, Nabil
López-Pires, Fabio
Barán, Benjamín
author Chamas, Nabil
author_facet Chamas, Nabil
López-Pires, Fabio
Barán, Benjamín
author_role author
author2 López-Pires, Fabio
Barán, Benjamín
author2_role author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
virtual machine placement
cloud computing
overbooking
elasticity
uncertainty
incremental vmp
vmp reconfiguration
topic Ciencias Informáticas
virtual machine placement
cloud computing
overbooking
elasticity
uncertainty
incremental vmp
vmp reconfiguration
dc.description.none.fl_txt_mv Cloud computing providers must support requests for resources in dynamic environments, considering service elasticity and overbooking of physical resources. Due to the randomness of requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. In this context, a renewed formulation of the VMP problem is presented, considering the optimization of four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. To solve the presented formulation, a two-phase optimization scheme is considered, composed by an online incremental VMP phase (iVMP) and an offline VMP reconfiguration (VMPr) phase. An experimental evaluation of five algorithms taking into account 400 different scenarios was performed, considering three VMPr Triggering and two VMPr Recovering methods as well as three VMPr resolution alternatives. Experimental results indicate which algorithm outperformed the other evaluated algorithms, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs.
Sociedad Argentina de Informática e Investigación Operativa (SADIO)
description Cloud computing providers must support requests for resources in dynamic environments, considering service elasticity and overbooking of physical resources. Due to the randomness of requests, Virtual Machine Placement (VMP) problems should be formulated under uncertainty. In this context, a renewed formulation of the VMP problem is presented, considering the optimization of four objective functions: (i) power consumption, (ii) economical revenue, (iii) resource utilization and (iv) reconfiguration time. To solve the presented formulation, a two-phase optimization scheme is considered, composed by an online incremental VMP phase (iVMP) and an offline VMP reconfiguration (VMPr) phase. An experimental evaluation of five algorithms taking into account 400 different scenarios was performed, considering three VMPr Triggering and two VMPr Recovering methods as well as three VMPr resolution alternatives. Experimental results indicate which algorithm outperformed the other evaluated algorithms, improving the quality of solutions in a scenario-based uncertainty model considering the following evaluation criteria: (i) average, (ii) maximum and (iii) minimum objective function costs.
publishDate 2017
dc.date.none.fl_str_mv 2017-09
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info:eu-repo/semantics/publishedVersion
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dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/65519
url http://sedici.unlp.edu.ar/handle/10915/65519
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
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Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-sa/4.0/
Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
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reponame_str SEDICI (UNLP)
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repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
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