Observatorio de I+D+i UPM

Memorias de investigación
Artículos en revistas:
An Autonomic Framework for Enhancing the Quality of Data Grid Services
Año:2012
Áreas de investigación
  • Ciencias de la computación y tecnología informática
Datos
Descripción
Data grid services have been used to deal with the increasing needs of applications in terms of data volume and throughput. The large scale, heterogeneity and dynamism of grid environments often make management and tuning of these data services very complex. Furthermore, current high-performance I/O approaches are characterized by their high complexity and specific features that usually require specialized administrator skills. Autonomic computing can help manage this complexity. The present paper describes an autonomic subsystem intended to provide self-management features aimed at efficiently reducing the I/O problem in a grid environment, thereby enhancing the quality of service (QoS) of data access and storage services in the grid. Our proposal takes into account that data produced in an I/O system is not usually immediately required. Therefore, performance improvements are related not only to current but also to any future I/O access, as the actual data access usually occurs later on. Nevertheless, the exact time of the next I/O operations is unknown. Thus, our approach proposes a long-term prediction designed to forecast the future workload of grid components. This enables the autonomic subsystem to determine the optimal data placement to improve both current and future I/O operations.
Internacional
Si
JCR del ISI
Si
Título de la revista
Future Generation Computer Systems-the International Journal of Grid Computing And Escience
ISSN
0167-739X
Factor de impacto JCR
1,978
Información de impacto
Volumen
DOI
Número de revista
Desde la página
1005
Hasta la página
1016
Mes
JULIO
Ranking
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Participantes
  • Autor: Maria de los Santos Perez Hernandez (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Grupo de Investigación: Ontology Engineering Group
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