An autonomic framework for enhancing the quality of data grid services

Sánchez Campos, Alberto and Montes, Jesús and Pérez Hernández, María de los Santos and 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.

Description

Title: An autonomic framework for enhancing the quality of data grid services
Author/s:
  • Sánchez Campos, Alberto
  • Montes, Jesús
  • Pérez Hernández, María de los Santos
  • Cortes Rosello, Antonio
Item Type: Article
Título de Revista/Publicación: Future Generation Computer Systems
Date: July 2012
ISSN: 0167-739X
Volume: 28
Subjects:
Freetext Keywords: 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.
Faculty: Facultad de Informática (UPM)
Department: Arquitectura y Tecnología de Sistemas Informáticos
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

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.

More information

Item ID: 16867
DC Identifier: http://oa.upm.es/16867/
OAI Identifier: oai:oa.upm.es:16867
DOI: 10.1016/j.future.2011.08.016
Official URL: http://www.sciencedirect.com/science/article/pii/S0167739X11002160
Deposited by: Memoria Investigacion
Deposited on: 06 Sep 2013 14:57
Last Modified: 21 Apr 2016 17:13
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