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Choi, Young-Ri
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dc.citation.endPage 380 -
dc.citation.number 3 -
dc.citation.startPage 366 -
dc.citation.title IEEE TRANSACTIONS ON SERVICES COMPUTING -
dc.citation.volume 10 -
dc.contributor.author Han, Jaeung -
dc.contributor.author Kim, Changdae -
dc.contributor.author Huh, Jaehyuk -
dc.contributor.author Jang, Gil-Jin -
dc.contributor.author Choi, Young-Ri -
dc.date.accessioned 2023-12-21T22:17:29Z -
dc.date.available 2023-12-21T22:17:29Z -
dc.date.created 2015-11-04 -
dc.date.issued 2017-05 -
dc.description.abstract With the advancement of cloud computing, there has been a growing interest in exploiting demand-based cloud resources for parallel scientific applications. To satisfy different needs for computing resources, cloud providers provide many different types of virtual machines (VMs) with various numbers of computing cores and amounts of memory. The cost and execution time of a scientific application vary depending on the types of VMs, number of VMs, and current status of the cloud due to interference among VMs. However, currently, cloud users are solely responsible for selecting the most effective VM configuration for their needs, but often end up with sub-optimal selections. In this paper, using molecular dynamics simulations as a case study, we propose a framework to guide users to select the optimal VM configurations that satisfy their requirements for scientific parallel computing in virtualized clusters. For molecular dynamics computation on a cluster of VMs, the guidance framework uses artificial neural networks which are trained to predict its execution times for various inputs, VM configurations, and status of interference among VMs. Using our performance prediction mechanisms, the guidance framework helps users choose an optimal or near-optimal VM cluster configuration under cost and runtime constraints. -
dc.identifier.bibliographicCitation IEEE TRANSACTIONS ON SERVICES COMPUTING, v.10, no.3, pp.366 - 380 -
dc.identifier.doi 10.1109/TSC.2015.2477835 -
dc.identifier.issn 1939-1374 -
dc.identifier.scopusid 2-s2.0-85027443631 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/17703 -
dc.identifier.url http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7254224 -
dc.identifier.wosid 000403437000004 -
dc.language 영어 -
dc.publisher IEEE COMPUTER SOC -
dc.title Configuration Guidance Framework for Molecular Dynamics Simulations in Virtualized Clusters -
dc.type Article -
dc.description.isOpenAccess FALSE -
dc.relation.journalWebOfScienceCategory Computer Science, Information Systems; Computer Science, Software Engineering -
dc.relation.journalResearchArea Computer Science -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordAuthor Cloud computing -
dc.subject.keywordAuthor scientific applications -
dc.subject.keywordAuthor virtual cluster configuration -
dc.subject.keywordAuthor molecular dynamics simulations -
dc.subject.keywordAuthor performance modeling and prediction -
dc.subject.keywordPlus CLOUDS -
dc.subject.keywordPlus SYSTEMS -

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