Dynamic on Demand Virtual Clusters in Grid

Autores
Bertogna, Mario Leandro; Grosclaude, Eduardo; Naiouf, Marcelo; De Giusti, Armando Eduardo; Luque Fadón, Emilio
Año de publicación
2008
Idioma
inglés
Tipo de recurso
documento de conferencia
Estado
versión publicada
Descripción
In Grid environments, many different resources are intended to work in a coordinated manner, each resource having its own features and complexity. As the number of resources grows, simplifying automation and management is among the most important issues to address. This paper's contribution lies on the extension and implementation of a grid metascheduler that dynamically discovers, creates and manages on-demand virtual clusters. The first module selects the clusters using graph heuristics. The algorithm then tries to find a solution by searching a set of clusters, mapped to the graph, that achieve the best performance for a given task. The second module, one per-grid node, monitors and manages physical and virtual machines. When a new task arrives, these modules modify virtual machine's configuration or use live migration to dynamically adapt resource distribution at the clusters, obtaining maximum utilization. Metascheduler components and local administrator modules work together to make decisions at run time to balance and optimize system throughput. This implementation results in performance improvement of 20% on the total computing time, with machines and clusters processing 100% of their working time. These results allow us to conclude that this solution is feasible to be implemented on Grid environments, where automation and self-management are key to attain effective resource usage.
Lecture Notes in Computer Science book series (LNTCS, vol. 5415)
Instituto de Investigación en Informática
Materia
Informática
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/143077

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spelling Dynamic on Demand Virtual Clusters in GridBertogna, Mario LeandroGrosclaude, EduardoNaiouf, MarceloDe Giusti, Armando EduardoLuque Fadón, EmilioInformáticaIn Grid environments, many different resources are intended to work in a coordinated manner, each resource having its own features and complexity. As the number of resources grows, simplifying automation and management is among the most important issues to address. This paper's contribution lies on the extension and implementation of a grid metascheduler that dynamically discovers, creates and manages on-demand virtual clusters. The first module selects the clusters using graph heuristics. The algorithm then tries to find a solution by searching a set of clusters, mapped to the graph, that achieve the best performance for a given task. The second module, one per-grid node, monitors and manages physical and virtual machines. When a new task arrives, these modules modify virtual machine's configuration or use live migration to dynamically adapt resource distribution at the clusters, obtaining maximum utilization. Metascheduler components and local administrator modules work together to make decisions at run time to balance and optimize system throughput. This implementation results in performance improvement of 20% on the total computing time, with machines and clusters processing 100% of their working time. These results allow us to conclude that this solution is feasible to be implemented on Grid environments, where automation and self-management are key to attain effective resource usage.Lecture Notes in Computer Science book series (LNTCS, vol. 5415)Instituto de Investigación en Informática2008info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf13-22http://sedici.unlp.edu.ar/handle/10915/143077enginfo:eu-repo/semantics/altIdentifier/issn/0302-9743info:eu-repo/semantics/altIdentifier/issn/1611-3349info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-00955-6_3info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-29T11:32:22Zoai:sedici.unlp.edu.ar:10915/143077Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-29 11:32:23.152SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Dynamic on Demand Virtual Clusters in Grid
title Dynamic on Demand Virtual Clusters in Grid
spellingShingle Dynamic on Demand Virtual Clusters in Grid
Bertogna, Mario Leandro
Informática
title_short Dynamic on Demand Virtual Clusters in Grid
title_full Dynamic on Demand Virtual Clusters in Grid
title_fullStr Dynamic on Demand Virtual Clusters in Grid
title_full_unstemmed Dynamic on Demand Virtual Clusters in Grid
title_sort Dynamic on Demand Virtual Clusters in Grid
dc.creator.none.fl_str_mv Bertogna, Mario Leandro
Grosclaude, Eduardo
Naiouf, Marcelo
De Giusti, Armando Eduardo
Luque Fadón, Emilio
author Bertogna, Mario Leandro
author_facet Bertogna, Mario Leandro
Grosclaude, Eduardo
Naiouf, Marcelo
De Giusti, Armando Eduardo
Luque Fadón, Emilio
author_role author
author2 Grosclaude, Eduardo
Naiouf, Marcelo
De Giusti, Armando Eduardo
Luque Fadón, Emilio
author2_role author
author
author
author
dc.subject.none.fl_str_mv Informática
topic Informática
dc.description.none.fl_txt_mv In Grid environments, many different resources are intended to work in a coordinated manner, each resource having its own features and complexity. As the number of resources grows, simplifying automation and management is among the most important issues to address. This paper's contribution lies on the extension and implementation of a grid metascheduler that dynamically discovers, creates and manages on-demand virtual clusters. The first module selects the clusters using graph heuristics. The algorithm then tries to find a solution by searching a set of clusters, mapped to the graph, that achieve the best performance for a given task. The second module, one per-grid node, monitors and manages physical and virtual machines. When a new task arrives, these modules modify virtual machine's configuration or use live migration to dynamically adapt resource distribution at the clusters, obtaining maximum utilization. Metascheduler components and local administrator modules work together to make decisions at run time to balance and optimize system throughput. This implementation results in performance improvement of 20% on the total computing time, with machines and clusters processing 100% of their working time. These results allow us to conclude that this solution is feasible to be implemented on Grid environments, where automation and self-management are key to attain effective resource usage.
Lecture Notes in Computer Science book series (LNTCS, vol. 5415)
Instituto de Investigación en Informática
description In Grid environments, many different resources are intended to work in a coordinated manner, each resource having its own features and complexity. As the number of resources grows, simplifying automation and management is among the most important issues to address. This paper's contribution lies on the extension and implementation of a grid metascheduler that dynamically discovers, creates and manages on-demand virtual clusters. The first module selects the clusters using graph heuristics. The algorithm then tries to find a solution by searching a set of clusters, mapped to the graph, that achieve the best performance for a given task. The second module, one per-grid node, monitors and manages physical and virtual machines. When a new task arrives, these modules modify virtual machine's configuration or use live migration to dynamically adapt resource distribution at the clusters, obtaining maximum utilization. Metascheduler components and local administrator modules work together to make decisions at run time to balance and optimize system throughput. This implementation results in performance improvement of 20% on the total computing time, with machines and clusters processing 100% of their working time. These results allow us to conclude that this solution is feasible to be implemented on Grid environments, where automation and self-management are key to attain effective resource usage.
publishDate 2008
dc.date.none.fl_str_mv 2008
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-642-00955-6_3
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
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