Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices

Autores
Rodriguez, Juan Manuel; Mateos Diaz, Cristian Maximiliano; Zunino Suarez, Alejandro Octavio
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Mobile devices have evolved from simple electronic agendas and mobile phones to small computers with great computational capabilities. In addition, there are more than 2 billion mobile devices around the world. Taking these facts into account, mobile devices are a potential source of computational resources for clusters and computational Grids. In this work, we present an analysis of different schedulers based on job stealing for mobile computational Grids. These job stealing techniques have been designed to consider energy consumption and battery status. As a result of this work, we present empirical evidence showing that energy-aware job stealing is more efficient than traditional random stealing in this context. In particular, our results show that mobile Grids using energy-aware job stealing might finish up to 11% more jobs than when using random stealing, and up to 24% more jobs than when not using any job stealing technique. This means that using energy-aware job stealing increases the energy efficiency of mobile computational Grids because it increases the number of jobs that can be executed using the same amount of energy.
Fil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Materia
Mobile Grid
Mobile Devices
Job Stealing
Cpu Intensive Application
Job Scheduling
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/6779
Acceder
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spelling Energy-efficient Job Stealing for CPU-intensive processing in Mobile DevicesRodriguez, Juan ManuelMateos Diaz, Cristian MaximilianoZunino Suarez, Alejandro OctavioMobile GridMobile DevicesJob StealingCpu Intensive ApplicationJob Schedulinghttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Mobile devices have evolved from simple electronic agendas and mobile phones to small computers with great computational capabilities. In addition, there are more than 2 billion mobile devices around the world. Taking these facts into account, mobile devices are a potential source of computational resources for clusters and computational Grids. In this work, we present an analysis of different schedulers based on job stealing for mobile computational Grids. These job stealing techniques have been designed to consider energy consumption and battery status. As a result of this work, we present empirical evidence showing that energy-aware job stealing is more efficient than traditional random stealing in this context. In particular, our results show that mobile Grids using energy-aware job stealing might finish up to 11% more jobs than when using random stealing, and up to 24% more jobs than when not using any job stealing technique. This means that using energy-aware job stealing increases the energy efficiency of mobile computational Grids because it increases the number of jobs that can be executed using the same amount of energy.Fil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaFil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; ArgentinaSpringer Wien2014-02info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/zipapplication/pdfapplication/zipapplication/zipapplication/pdfapplication/ziphttp://hdl.handle.net/11336/6779Rodriguez, Juan Manuel; Mateos Diaz, Cristian Maximiliano; Zunino Suarez, Alejandro Octavio; Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices; Springer Wien; Computing; 96; 2; 2-2014; 87-1170010-485Xenginfo:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007%2Fs00607-012-0245-5info:eu-repo/semantics/altIdentifier/doi/info:eu-repo/semantics/altIdentifier/doi/10.1007/s00607-012-0245-5info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-17T11:37:37Zoai:ri.conicet.gov.ar:11336/6779instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-17 11:37:38.16CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices
title Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices
spellingShingle Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices
Rodriguez, Juan Manuel
Mobile Grid
Mobile Devices
Job Stealing
Cpu Intensive Application
Job Scheduling
title_short Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices
title_full Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices
title_fullStr Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices
title_full_unstemmed Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices
title_sort Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices
dc.creator.none.fl_str_mv Rodriguez, Juan Manuel
Mateos Diaz, Cristian Maximiliano
Zunino Suarez, Alejandro Octavio
author Rodriguez, Juan Manuel
author_facet Rodriguez, Juan Manuel
Mateos Diaz, Cristian Maximiliano
Zunino Suarez, Alejandro Octavio
author_role author
author2 Mateos Diaz, Cristian Maximiliano
Zunino Suarez, Alejandro Octavio
author2_role author
author
dc.subject.none.fl_str_mv Mobile Grid
Mobile Devices
Job Stealing
Cpu Intensive Application
Job Scheduling
topic Mobile Grid
Mobile Devices
Job Stealing
Cpu Intensive Application
Job Scheduling
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Mobile devices have evolved from simple electronic agendas and mobile phones to small computers with great computational capabilities. In addition, there are more than 2 billion mobile devices around the world. Taking these facts into account, mobile devices are a potential source of computational resources for clusters and computational Grids. In this work, we present an analysis of different schedulers based on job stealing for mobile computational Grids. These job stealing techniques have been designed to consider energy consumption and battery status. As a result of this work, we present empirical evidence showing that energy-aware job stealing is more efficient than traditional random stealing in this context. In particular, our results show that mobile Grids using energy-aware job stealing might finish up to 11% more jobs than when using random stealing, and up to 24% more jobs than when not using any job stealing technique. This means that using energy-aware job stealing increases the energy efficiency of mobile computational Grids because it increases the number of jobs that can be executed using the same amount of energy.
Fil: Rodriguez, Juan Manuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Mateos Diaz, Cristian Maximiliano. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
Fil: Zunino Suarez, Alejandro Octavio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Instituto Superior de Ingenieria del Software; Argentina
description Mobile devices have evolved from simple electronic agendas and mobile phones to small computers with great computational capabilities. In addition, there are more than 2 billion mobile devices around the world. Taking these facts into account, mobile devices are a potential source of computational resources for clusters and computational Grids. In this work, we present an analysis of different schedulers based on job stealing for mobile computational Grids. These job stealing techniques have been designed to consider energy consumption and battery status. As a result of this work, we present empirical evidence showing that energy-aware job stealing is more efficient than traditional random stealing in this context. In particular, our results show that mobile Grids using energy-aware job stealing might finish up to 11% more jobs than when using random stealing, and up to 24% more jobs than when not using any job stealing technique. This means that using energy-aware job stealing increases the energy efficiency of mobile computational Grids because it increases the number of jobs that can be executed using the same amount of energy.
publishDate 2014
dc.date.none.fl_str_mv 2014-02
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/6779
Rodriguez, Juan Manuel; Mateos Diaz, Cristian Maximiliano; Zunino Suarez, Alejandro Octavio; Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices; Springer Wien; Computing; 96; 2; 2-2014; 87-117
0010-485X
url http://hdl.handle.net/11336/6779
identifier_str_mv Rodriguez, Juan Manuel; Mateos Diaz, Cristian Maximiliano; Zunino Suarez, Alejandro Octavio; Energy-efficient Job Stealing for CPU-intensive processing in Mobile Devices; Springer Wien; Computing; 96; 2; 2-2014; 87-117
0010-485X
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://link.springer.com/article/10.1007%2Fs00607-012-0245-5
info:eu-repo/semantics/altIdentifier/doi/
info:eu-repo/semantics/altIdentifier/doi/10.1007/s00607-012-0245-5
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/zip
application/pdf
application/zip
application/zip
application/pdf
application/zip
dc.publisher.none.fl_str_mv Springer Wien
publisher.none.fl_str_mv Springer Wien
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.001348

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