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Grid workflow optimization regarding dynamically changing resources and conditions

Grid workflow optimization regarding dynamically changing resources and conditions Automatic construction of workflows on the Grid is a challenging task. The problems that have to be solved are manifold: How can existing services be integrated into a workflow that is able to accomplish a specific task? How can an optimal workflow be constructed with respect to changing resource characteristics during the optimization process? How to cope with dynamically changing or incomplete knowledge of the goal function of the optimization process? and finally: How to react to service failures during workflow execution? In this paper, we propose a method to optimize a workflow based on a heuristic A* approach that allows to react to dynamics in the environment. Changes in the Grid infrastructure and in the users' requirements can be handled during the optimization process as well as during the execution of the workflow. Our algorithm also allows the workflow to recover from failing resources during the execution phase. Copyright © 2008 John Wiley & Sons, Ltd. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Concurrency and Computation: Practice & Experience Wiley

Grid workflow optimization regarding dynamically changing resources and conditions

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References (16)

Publisher
Wiley
Copyright
Copyright © 2008 John Wiley & Sons, Ltd.
ISSN
1532-0626
eISSN
1532-0634
DOI
10.1002/cpe.1317
Publisher site
See Article on Publisher Site

Abstract

Automatic construction of workflows on the Grid is a challenging task. The problems that have to be solved are manifold: How can existing services be integrated into a workflow that is able to accomplish a specific task? How can an optimal workflow be constructed with respect to changing resource characteristics during the optimization process? How to cope with dynamically changing or incomplete knowledge of the goal function of the optimization process? and finally: How to react to service failures during workflow execution? In this paper, we propose a method to optimize a workflow based on a heuristic A* approach that allows to react to dynamics in the environment. Changes in the Grid infrastructure and in the users' requirements can be handled during the optimization process as well as during the execution of the workflow. Our algorithm also allows the workflow to recover from failing resources during the execution phase. Copyright © 2008 John Wiley & Sons, Ltd.

Journal

Concurrency and Computation: Practice & ExperienceWiley

Published: Oct 1, 2008

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