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Dynamic self-configuring deployment of partitioned applications

Dynamic self-configuring deployment of partitioned applications


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Anthony, Richard (2005) Dynamic self-configuring deployment of partitioned applications. In: DCABES and ICPACE Joint Conference on Distributed Algorithms for Science and Engineering. University of Greenwich, CMS Press, Greenwich, London, UK, pp. 187-190. ISBN 1904521274

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Loosely-coupled clusters and grids are popular platforms for parallel processing as they have good cost-performance ratios, are scalable and abundant. However, these platforms represent highly dynamic environments. Performance and efficiency can be seriously impacted by environmental perturbations, especially where the run-time configuration has been decided statically, either at compile time or at the start of execution. Autonomic computing advocates self-managing behaviour in which applications continuously modify their behaviour to suit their environment and context. This approach reduces the emphasis on the pre-configuration of components and relies instead on inbuild learning and discovery capabilities. This paper reports part of an investigation into the extend to which the autonomic approach can be beneficial to parallel processing in loosely-coupled environments.

Item Type: Conference Proceedings
Title of Proceedings: DCABES and ICPACE Joint Conference on Distributed Algorithms for Science and Engineering
Additional Information: [1] This paper was first presented at the Joint DCABES and ICPACE meeting on distributed algorithms for science and engineering held from 25th-27th August 2005 at the University of Greenwich, London, UK.
Uncontrolled Keywords: parallel processing, pre-configuration of components, autonomic approach, electronic data processing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Pre-2014 Departments: School of Computing & Mathematical Sciences
Related URLs:
Last Modified: 14 Oct 2016 09:14
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None

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