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Mitigation Technique for Performance Degradation of Virtual Machine Owing to GPU Pass-Through in Fog Computing

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dc.contributor.authorKang, Jihun-
dc.contributor.authorYu, Heonchang-
dc.date.accessioned2021-09-02T10:37:58Z-
dc.date.available2021-09-02T10:37:58Z-
dc.date.created2021-06-19-
dc.date.issued2018-06-
dc.identifier.issn1229-2370-
dc.identifier.urihttps://scholar.korea.ac.kr/handle/2021.sw.korea/75043-
dc.description.abstractAs the size of data increases and computation becomes complicated in fog computing environments, the need for high-performance computation is increasing. One of the most popular ways to improve the performance of a virtual machine (VM) is to allocate a graphic processing unit (GPU) to the VM for supporting general purpose computing on graphic processing unit (GPGPU) operations. The direct pass-through, often used for GPUs in VMs, is popular in the cloud because VMs can use the full functionality of the GPU and experience virtually no performance degradation owing to virtualization. Direct pass-through is very useful for improving the performance of VMs. However, since the GPU usage time is not considered in the VM scheduler that operates based on the central processing unit (CPU) usage time of the VM, the VM performing the GPGPU operation degrades the performance of other VMs. In this paper, we analyze the effect of the VM performing the GPGPU operation (GPGPU-intensive VM) on other VMs through experiments. Then, we propose a method to mitigate the performance degradation of other VMs by dynamically allocating the resource usage time of the VM and preventing the priority preemption of the GPGPU-intensive VM.-
dc.languageEnglish-
dc.language.isoen-
dc.publisherKOREAN INST COMMUNICATIONS SCIENCES (K I C S)-
dc.titleMitigation Technique for Performance Degradation of Virtual Machine Owing to GPU Pass-Through in Fog Computing-
dc.typeArticle-
dc.contributor.affiliatedAuthorYu, Heonchang-
dc.identifier.doi10.1109/JCN.2018.000038-
dc.identifier.scopusid2-s2.0-85052338825-
dc.identifier.wosid000442356100004-
dc.identifier.bibliographicCitationJOURNAL OF COMMUNICATIONS AND NETWORKS, v.20, no.3, pp.257 - 265-
dc.relation.isPartOfJOURNAL OF COMMUNICATIONS AND NETWORKS-
dc.citation.titleJOURNAL OF COMMUNICATIONS AND NETWORKS-
dc.citation.volume20-
dc.citation.number3-
dc.citation.startPage257-
dc.citation.endPage265-
dc.type.rimsART-
dc.type.docTypeArticle-
dc.identifier.kciidART002366209-
dc.description.journalClass1-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.description.journalRegisteredClasskci-
dc.relation.journalResearchAreaComputer Science-
dc.relation.journalResearchAreaTelecommunications-
dc.relation.journalWebOfScienceCategoryComputer Science, Information Systems-
dc.relation.journalWebOfScienceCategoryTelecommunications-
dc.subject.keywordAuthorFog computing-
dc.subject.keywordAuthorgeneral purpose computing on graphic processing unit (GPGPU)-
dc.subject.keywordAuthorperformance isolation-
dc.subject.keywordAuthorvirtualization-
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