Grid Global Behavior Prediction

Montes, Jesús and Sánchez, Alberto and Pérez Hernández, María de los Santos (2011). Grid Global Behavior Prediction. In: "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.

Description

Title: Grid Global Behavior Prediction
Author/s:
  • Montes, Jesús
  • Sánchez, Alberto
  • Pérez Hernández, María de los Santos
Item Type: Presentation at Congress or Conference (Article)
Event Title: The 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Event Dates: 23/05/2011 - 25/05/2011
Event Location: Newport Beach, EEUU
Title of Book: Proceedings of the 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing
Date: 2011
ISBN: 978-0-7695-4395-6
Subjects:
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

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.

More information

Item ID: 12096
DC Identifier: http://oa.upm.es/12096/
OAI Identifier: oai:oa.upm.es:12096
DOI: 10.1109/CCGrid.2011.17
Official URL: http://www.ics.uci.edu/~ccgrid11/
Deposited by: Memoria Investigacion
Deposited on: 12 Sep 2012 12:04
Last Modified: 21 Apr 2016 11:16
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