CyberinfraV1, Main, Exploration, bibRecord, 000950

Self-adaptive Architectures for Autonomic Computational Science

Identifieur interne : 000950 ( Main/Exploration ); précédent : 000949; suivant : 000951

Self-adaptive Architectures for Autonomic Computational Science

Auteurs : Shantenu Jha [États-Unis, Royaume-Uni] ; Manish Parashar [États-Unis] ; Omer Rana [Royaume-Uni]

Source :

Lecture Notes in Computer Science [ 0302-9743 ] ; 2010.

RBID : ISTEX:CC0282BAA02055F1E6983018942EB30E95F3991F

Abstract

Abstract: Self-adaptation enables a system to modify it’s behaviour based on changes in its operating environment. Such a system must utilize monitoring information to determine how to respond either through a systems administrator or automatically (based on policies pre-defined by an administrator) to such changes. In computational science applications that utilize distributed infrastructure (such as Computational Grids and Clouds), dealing with heterogeneity and scale of the underlying infrastructure remains a challenge. Many applications that do adapt to changes in underlying operating environments often utilize ad hoc, application-specific approaches. The aim of this work is to generalize from existing examples, and thereby lay the foundation for a framework for Autonomic Computational Science (ACS). We use two existing applications – Ensemble Kalman Filtering and Coupled Fusion Simulation – to describe a conceptual framework for ACS, consisting of mechanisms, strategies and objectives, and demonstrate how these concepts can be used to more effectively realize pre-defined application objectives.

Url:

https://api.istex.fr/document/CC0282BAA02055F1E6983018942EB30E95F3991F/fulltext/pdf

DOI: 10.1007/978-3-642-14412-7_9

Affiliations:

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<front><div type="abstract" xml:lang="en">Abstract: Self-adaptation enables a system to modify it’s behaviour based on changes in its operating environment. Such a system must utilize monitoring information to determine how to respond either through a systems administrator or automatically (based on policies pre-defined by an administrator) to such changes. In computational science applications that utilize distributed infrastructure (such as Computational Grids and Clouds), dealing with heterogeneity and scale of the underlying infrastructure remains a challenge. Many applications that do adapt to changes in underlying operating environments often utilize ad hoc, application-specific approaches. The aim of this work is to generalize from existing examples, and thereby lay the foundation for a framework for Autonomic Computational Science (ACS). We use two existing applications – Ensemble Kalman Filtering and Coupled Fusion Simulation – to describe a conceptual framework for ACS, consisting of mechanisms, strategies and objectives, and demonstrate how these concepts can be used to more effectively realize pre-defined application objectives.</div>
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Self-adaptive Architectures for Autonomic Computational Science

Self-adaptive Architectures for Autonomic Computational Science

Source :

Abstract

Links toward previous steps (curation, corpus...)

Le document en format XML

Pour manipuler ce document sous Unix (Dilib)

Pour mettre un lien sur cette page dans le réseau Wicri