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AuthorLeznik, Markdc.contributor.author
AuthorVolpert, Simondc.contributor.author
AuthorGriesinger, Frankdc.contributor.author
AuthorSeybold, Danieldc.contributor.author
AuthorDomaschka, Jörgdc.contributor.author
Date of accession2018-12-10T08:55:13Zdc.date.accessioned
Available in OPARU since2018-12-10T08:55:13Zdc.date.available
Date of first publication2018-08-16dc.date.issued
AbstractWith the rapid rise of the cloud computing paradigm, the manual maintenance and provisioning of the technological layers behind it, both in their hardware and virtualized form, became cumbersome and error- prone. This has opened up the need for automated capacity planning strategies in heterogeneous cloud computing environments. However, even with mechanisms to fully accommodate customers and ful ll service- level agreements, providers often tend to over-provision their hardware and virtual resources. A proliferation of unused capacity leads to higher energy costs, and correspondingly, the price for cloud technology services. Capacity planning algorithms rely on data collected from the utilized resources. Yet, the amount of data aggregated through the monitoring of hardware and virtual instances does not allow for a manual supervision, much less data analysis or a correlation and anomaly detection. Current data science advancements enable the assistance of e cient automation, scheduling and provisioning of cloud computing resources based on supervised and unsupervised machine learning techniques. In this work, we present the current state of the art in monitoring, storage, analysis and adaptation approaches for the data produced by cloud computing environments, to enable proactive, dynamic resource provisioning.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
Earlier versionhttp://dx.doi.org/10.18725/OPARU-9631dc.relation.isversionof
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordIaaSdc.subject
KeywordPaaSdc.subject
KeywordAutonomicsdc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHInformation technology; Managementdc.subject.lcsh
LCSHService-oriented architecture (Computer science)dc.subject.lcsh
LCSHComputing platformsdc.subject.lcsh
LCSHMachine learningdc.subject.lcsh
TitleDone yet? A critical introspective of the cloud management toolboxdc.title
Resource typeBeitrag zu einer Konferenzdc.type
VersionacceptedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-10602dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-10659-0dc.identifier.urn
GNDCloud Computingdc.subject.gnd
GNDData Sciencedc.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/ICE.2018.8436348dc.relation1.doi
Source - Title of source2018 IEEE International Conference on Engineering, Technology and Innovation (ICE/ITMC)source.title
Source - Volume2018source.volume
Source - Year2018source.year
Source - ISBN978-1-5386-1470-9source.identifier.isbn
Source - ISBN978-1-5386-1469-3source.identifier.isbn
EU project uulmMELODIC / Multi-cloud Execution-ware for Large-scale Optimized Data-Intensive Computing / EC / H2020 / 731664uulm.projectEU
EU project uulmRECAP / Reliable Capacity Provisioning and Enhanced Remediation for Distributed Cloud Applications / EC / H2020 / 732667uulm.projectEU
Conference nameICE/IEEE ITMC International Conference on Engineering, Technology and Innovationuulm.conferenceName
Conference placeStuttgartuulm.conferencePlace
Conference start date2018-06-17uulm.conferenceStartDate
Conference end date2018-06-20uulm.conferenceEndDate
Bibliographyuulmuulm.bibliographie


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