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Cloud Workload Prediction by Means of Simulations

Kecskemeti, G and Kertesz, A and Nemeth, Z (2017) Cloud Workload Prediction by Means of Simulations. In: Proceedings of the Computing Frontiers Conference . pp. 279-282. (ACM International Conference on Computing Frontiers 2017, 15 May 2017 - 17 May 2017, Siena, Italy).

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Abstract

Clouds hide the complexity of maintaining a physical infrastructure with a disadvantage: they also hide their internal workings. Should users need to know about these details e.g., to increase the reliability or performance of their applications, they would need to detect slight behavioural changes in the underlying system. Existing solutions for such purposes offer limited capabilities. This paper proposes a technique for predicting background workload by means of simulations that are providing knowledge of the underlying clouds to support activities like cloud orchestration or workflow enactment. We propose these predictions to select more suitable execution environments for scientific workflows. We validate the proposed prediction approach with a biochemical application.

Item Type: Conference or Workshop Item (Paper)
Additional Information: ©2017 ACM. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Proceedings of the Computing Frontiers Conference , http://doi.acm.org/10.1145/3075564.3075589
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science
Publisher: ACM Digital Library
Date Deposited: 06 Apr 2017 11:01
Last Modified: 10 Aug 2017 11:02
DOI or Identification number: 10.1145/3075564.3075589
URI: http://researchonline.ljmu.ac.uk/id/eprint/6219

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