Grid Global Behavior Prediction

Montes, Jesús; Sánchez, Alberto y Pérez Hernández, María de los Santos (2011). Grid Global Behavior Prediction. En: "The 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing", 23/05/2011 - 25/05/2011, Newport Beach, EEUU. ISBN 978-0-7695-4395-6. pp. 124-133. https://doi.org/10.1109/CCGrid.2011.17.

Descripción

Título: Grid Global Behavior Prediction
Autor/es:
  • Montes, Jesús
  • Sánchez, Alberto
  • Pérez Hernández, María de los Santos
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: The 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Fechas del Evento: 23/05/2011 - 25/05/2011
Lugar del Evento: Newport Beach, EEUU
Título del Libro: Proceedings of the 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Fecha: 2011
ISBN: 978-0-7695-4395-6
Materias:
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

Complexity has always been one of the most important issues in distributed computing. From the first clusters to grid and now cloud computing, dealing correctly and efficiently with system complexity is the key to taking technology a step further. In this sense, global behavior modeling is an innovative methodology aimed at understanding the grid behavior. The main objective of this methodology is to synthesize the grid's vast, heterogeneous nature into a simple but powerful behavior model, represented in the form of a single, abstract entity, with a global state. Global behavior modeling has proved to be very useful in effectively managing grid complexity but, in many cases, deeper knowledge is needed. It generates a descriptive model that could be greatly improved if extended not only to explain behavior, but also to predict it. In this paper we present a prediction methodology whose objective is to define the techniques needed to create global behavior prediction models for grid systems. This global behavior prediction can benefit grid management, specially in areas such as fault tolerance or job scheduling. The paper presents experimental results obtained in real scenarios in order to validate this approach.

Más información

ID de Registro: 12096
Identificador DC: http://oa.upm.es/12096/
Identificador OAI: oai:oa.upm.es:12096
Identificador DOI: 10.1109/CCGrid.2011.17
URL Oficial: http://www.ics.uci.edu/~ccgrid11/
Depositado por: Memoria Investigacion
Depositado el: 12 Sep 2012 12:04
Ultima Modificación: 21 Abr 2016 11:16
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