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dc.contributor.authorDehury, Chinmaya Kumar
dc.contributor.authorChhetri, Tek Raj
dc.contributor.authorLind, Artjom
dc.contributor.authorSrirama, Satish Narayana
dc.contributor.authorFensel, Anna
dc.date.accessioned2021-12-07T14:22:23Z
dc.date.available2021-12-07T14:22:23Z
dc.date.issued2021
dc.identifier.urihttps://datadoi.ee/handle/33/425
dc.identifier.urihttps://doi.org/10.23673/re-300
dc.description.abstractThis data is in support of the research on "A combined system metrics approach to cloud service reliability using artificial intelligence" (doi: 10.20944/preprints202111.0548.v1)en
dc.formatCSVen
dc.formatZIPen
dc.language.isoenen
dc.publisherSemantic Technology Institute (STI) Innsbruck, Department of Computer Science, University of Innsbruck, Austria; Institute of Computer Science, University of Tartu, Estonia; School of Computer and Information Sciences, University of Hyderabad, Indiaen
dc.relation10.20944/preprints202111.0548.v1en
dc.relationhttps://doi.org/10.3390/bdcc6010026
dc.rightsinfo:eu-repo/semantics/openAccessen
dc.subjectFailure Predictionen
dc.subjectFault-toleranceen
dc.subjectCloud computingen
dc.subjectArtificial Intelligenceen
dc.subjectReliabilityen
dc.titleHPC Cloud traces for better cloud service reliabilityen
dc.typeinfo:eu-repo/semantics/dataseten


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