An autonomic framework for enhancing the quality of data grid services

Sánchez Campos, Alberto; Montes, Jesús; Pérez Hernández, María de los Santos y Cortes Rosello, Antonio (2012). An autonomic framework for enhancing the quality of data grid services. "Future Generation Computer Systems", v. 28 (n. 7); pp. 1005-1016. ISSN 0167-739X. https://doi.org/10.1016/j.future.2011.08.016.

Descripción

Título: An autonomic framework for enhancing the quality of data grid services
Autor/es:
  • Sánchez Campos, Alberto
  • Montes, Jesús
  • Pérez Hernández, María de los Santos
  • Cortes Rosello, Antonio
Tipo de Documento: Artículo
Título de Revista/Publicación: Future Generation Computer Systems
Fecha: Julio 2012
Volumen: 28
Materias:
Palabras Clave Informales: Autonomic storage, Almacenamiento automático, Autogestión, Self-management, Data-intensive applications, aplicaciones de uso intensivo de datos, Data grids, Redes de datos, Quality of service (QoS), Servicios de calidad, Predicciones a largo plazo, Long-term prediction.
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Arquitectura y Tecnología de Sistemas Informáticos
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 16867
Identificador DC: http://oa.upm.es/16867/
Identificador OAI: oai:oa.upm.es:16867
Identificador DOI: 10.1016/j.future.2011.08.016
URL Oficial: http://www.sciencedirect.com/science/article/pii/S0167739X11002160
Depositado por: Memoria Investigacion
Depositado el: 06 Sep 2013 14:57
Ultima Modificación: 21 Abr 2016 17:13
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