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An autonomic framework for enhancing the quality of data grid services

dc.contributor.authorSanchez, Alberto
dc.contributor.authorMostes, Jesus
dc.contributor.authorS. Perez, Maria
dc.contributor.authorCortes, Toni
dc.identifier.citationAlberto Sánchez, Jesús Montes, María S. Pérez, Toni Cortes, An autonomic framework for enhancing the quality of data grid services, Future Generation Computer Systems, Volume 28, Issue 7, July 2012, Pages 1005-1016, ISSN 0167-739X,
dc.description.abstractData 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
dc.description.sponsorshipThis work is partially supported by the Madrid Regional Authority (Comunidad de Madrid) and the Universidad Rey Juan Carlos under the URJC-CM-2010-CET-5185
dc.relation.ispartofseriesVolume 28;Issue 7
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subjectAutonomic storagees
dc.subjectData-intensive applicationses
dc.subjectData gridses
dc.subjectQuality of service (QoS)es
dc.subjectLong-term predictiones
dc.titleAn autonomic framework for enhancing the quality of data grid serviceses
dc.subject.unesco3304.06 Arquitectura de Ordenadoreses
dc.subject.unesco1203.17 Informáticaes
dc.description.departamentoArquitectura de Computadores y Ciencias de la Computación e Inteligencia Artificial

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