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AuthorHauser, Christopher B.dc.contributor.author
AuthorDomaschka, Jörgdc.contributor.author
AuthorWesner, Stefandc.contributor.author
Date of accession2018-09-20T11:09:17Zdc.date.accessioned
Available in OPARU since2018-09-20T11:09:17Zdc.date.available
Date of first publication2018-08-13dc.date.issued
AbstractCloud data centres share physical resources at the same time with multiple users, which can lead to resource interferences. Especially with resource intensive computations like HPC or big data processing jobs, neighbouring applications in a cloud data centre may experience less performance of their assigned virtual resources. This work evaluates the predictability of such resource intensive jobs in principle. The assumption is, that the execution behaviour of such computations depends on the computation and the environment parameters. From these two influencing factors, the predictability is the outcome of removing the hardware dependent environment parameters from the observed execution behaviour, in order to compute any other execution behaviour for computations with similar computation parameters but on a different environment. The assumptions are analysed and evaluated with the HPC application Molpro.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordPredictiondc.subject
KeywordResource intensive jobsdc.subject
KeywordResource interferencedc.subject
KeywordHPCdc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHHigh performance computingdc.subject.lcsh
LCSHParallel processing (Electronic computers)dc.subject.lcsh
LCSHData librariesdc.subject.lcsh
TitlePredictability of resource intensive big data and HPC jobs in cloud data centresdc.title
Resource typeBeitrag zu einer Konferenzdc.type
VersionacceptedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-9865dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-9922-1dc.identifier.urn
GNDCloud Computingdc.subject.gnd
GNDHochleistungsrechnendc.subject.gnd
GNDVorhersagbarkeitdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Organisation und Management von Informationssystemenuulm.affiliationSpecific
Peer reviewjauulm.peerReview
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
DOI of original publication10.1109/QRS-C.2018.00069dc.relation1.doi
Source - Title of source2018 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)source.title
Quellenangabe - HerausgeberInstitute of Electrical and Electronics Engineerssource.contributor.editor1
Source - Place of publicationInstitute of Electrical and Electronics Engineerssource.publisher
Source - Volume2018source.volume
Source - Year2018source.year
Source - ISBN978-1-5386-7839-8source.identifier.isbn
Source - ISBN978-1-5386-7840-4source.identifier.isbn
EU project uulmCACTOS / Context-Aware Cloud Topology Optimisation and Simulation / EC / FP7 / 610711uulm.projectEU
Conference name18th IEEE International Conference on Software Quality, Reliability, and Security Companion (QRS-C)uulm.conferenceName
Conference placeLissabonuulm.conferencePlace
Conference start date2018-07-16uulm.conferenceStartDate
Conference end date2018-07-20uulm.conferenceEndDate
Bibliographyuulmuulm.bibliographie


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