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Hierarchical recursive resource sharing for containerized applications

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dc.contributor.authorKim, Young Jin-
dc.contributor.authorLee, Young Choon-
dc.contributor.authorHan, Hyuck-
dc.contributor.authorKang, Sooyong-
dc.date.accessioned2022-07-10T22:59:24Z-
dc.date.available2022-07-10T22:59:24Z-
dc.date.created2021-05-13-
dc.date.issued2018-11-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/149019-
dc.description.abstractApplications are increasingly containerized using techniques, such as LXC and Docker. Scientific workflow applications are no exception. In this paper, we address the problem of resource contention between concurrently running containerized scientific workflows. To this end, we design and implement Hierarchical Recursive Resource Sharing (HRRS), which structures multiple concurrent containers in a hierarchy that automatically and dynamically regulates their resource consumption based on their level/tier in the hierarchy. The hierarchy is recursively updated as the top-tier container completes its execution with the second-tier container becoming the top-tier container inheriting the resource consumption priority. We have evaluated the performance of HRRS using multiple large-scale scientific workflows containerized by Docker. The experimental results show the significant reduction of resource contention as evident in performance improvement of 49%, 160% and 18% compared with sequential execution, concurrent execution with fair resource share and execution with submission interval, respectively.-
dc.language영어-
dc.language.isoen-
dc.publisherSpringer Verlag-
dc.titleHierarchical recursive resource sharing for containerized applications-
dc.typeArticle-
dc.contributor.affiliatedAuthorKang, Sooyong-
dc.identifier.doi10.1007/978-3-030-03596-9_56-
dc.identifier.scopusid2-s2.0-85056843327-
dc.identifier.bibliographicCitationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.11236 LNCS, pp.781 - 796-
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.volume11236 LNCS-
dc.citation.startPage781-
dc.citation.endPage796-
dc.type.rimsART-
dc.type.docTypeConference Paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordPlusDistributed computer systems-
dc.subject.keywordPlusConcurrent execution-
dc.subject.keywordPlusDesign and implements-
dc.subject.keywordPlusPerformance improvements-
dc.subject.keywordPlusResource consumption-
dc.subject.keywordPlusResource contention-
dc.subject.keywordPlusResource sharing-
dc.subject.keywordPlusScientific workflows-
dc.subject.keywordPlusSequential execution-
dc.subject.keywordPlusContainers-
dc.subject.keywordAuthorContainerized scientific workflows-
dc.subject.keywordAuthorContainers-
dc.subject.keywordAuthorResource sharing-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-030-03596-9_56-
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