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GA-ETI: An enhanced genetic algorithm for the scheduling of scientific workflows in cloud environments
The University of Sydney, Sydney, Australia.
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science (from 2013).ORCID iD: 0000-0001-9194-010X
CSIRO, Australia.
School of Computer Science, China University of Geosciences, China.
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2018 (English)In: Journal of Computational Science, ISSN 1877-7503, E-ISSN 1877-7511, Vol. 26, p. 318-331Article in journal (Refereed) Published
Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 26, p. 318-331
Keywords [en]
Cloud computing; Scientific workflow; Scheduling algorithms; Genetic algorithm; Virtual machine
National Category
Computer Sciences
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-45845DOI: 10.1016/j.jocs.2016.08.007ISI: 000438001600033OAI: oai:DiVA.org:kau-45845DiVA, id: diva2:968107
Available from: 2016-09-12 Created: 2016-09-12 Last updated: 2018-08-16Bibliographically approved

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Taheri, Javid

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