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Self-adaptive architectures for autonomic computational science

Jha, Shantenu, Parashar, Manish and Rana, Omer Farood 2010. Self-adaptive architectures for autonomic computational science. Lecture Notes in Computer Science (6090) , pp. 177-197. 10.1007/978-3-642-14412-7_9

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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.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Additional Information: First International Workshop, SOAR 2009, Cambridge, UK, September 14, 2009, Revised Selected and Invited Papers
Publisher: Springer Verlag
ISSN: 0302-9743
Last Modified: 04 Jun 2017 03:49

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