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
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/143077
Ver los metadatos del registro completo
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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. |
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2008 |
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2008 |
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