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AuthorBaur, Danieldc.contributor.author
AuthorGriesinger, Frankdc.contributor.author
AuthorVerginadis, Yiannisdc.contributor.author
AuthorStefanidis, Vasilisdc.contributor.author
AuthorPatiniotakis, Ioannisdc.contributor.author
Date of accession2020-03-11T15:45:04Zdc.date.accessioned
Available in OPARU since2020-03-11T15:45:04Zdc.date.available
Date of first publication2019-01-07dc.date.issued
AbstractCloud computing and its computing as a utility paradigm offers on-demand resources, enabling its users to seamlessly adapt applications to the current demand. With its (virtually) unlimited elasticity, managing deployed applications becomes more and more complex raising the need for automation. Such autonomous systems leverage the importance to constantly monitor and analyse the deployed workload and the underlying infrastructure serving as knowledge-base for deriving corrective actions like scaling. Existing monitoring solutions, however are not designed to cope with a frequently changing topology. We propose a monitoring and event processing framework following a model-driven approach, that allows users to express i) the monitoring demand by directly referencing entities of the deployment context, ii) aggregate the monitoring data using mathematical expressions, iii) trigger and process events based on the monitoring data and finally iv) attach scalability rules to those events. We accompany the modelling language with a monitoring orchestration and distributed complex event processing framework, capable of enacting the model in a frequently changing multi-cloud infrastructure, considering cloud-specific aspects like communication costs.dc.description.abstract
Languageendc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordComplex event processingdc.subject
Dewey Decimal GroupDDC 004 / Data processing & computer sciencedc.subject.ddc
LCSHCloud computingdc.subject.lcsh
LCSHMonitoringdc.subject.lcsh
LCSHEvent processing (Computer science)dc.subject.lcsh
LCSHProgramming (Mathematics)dc.subject.lcsh
TitleA model driven engineering approach for flexible and distributed monitoring of cross-cloud applicationsdc.title
Resource typeBeitrag zu einer Konferenzdc.type
VersionacceptedVersiondc.description.version
DOIhttp://dx.doi.org/10.18725/OPARU-26005dc.identifier.doi
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-26068-3dc.identifier.urn
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Organisation und Management von Informationssystemenuulm.affiliationSpecific
Peer reviewjauulm.peerReview
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
In cooperation withNational Technical University of Athensuulm.cooperation
DOI of original publication10.1109/UCC.2018.00012dc.relation1.doi
Source - Title of source2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)source.title
Source - Place of publicationInstitute of Electrical and Electronics Engineerssource.publisher
Source - Volume2018source.volume
Source - From page31source.fromPage
Source - To page40source.toPage
Source - ISBN978-1-5386-5504-7source.identifier.isbn
Source - ISBN978-1-5386-5505-4source.identifier.isbn
EU projectMELODIC / Multi-cloud Execution-ware for Large-scale Optimized Data-Intensive Computing / EC / H2020 / 731664uulm.projectEU
EU projectRECAP / Reliable Capacity Provisioning and Enhanced Remediation for Distributed Cloud Applications / EC / H2020 / 732667uulm.projectEU
EU projectCloudPerfect / Enabling CLoud Orchestration, Performance and Cost Efficiency Tools for QoE Enhancement and Provider Ranking / EC / H2020 / 732258uulm.projectEU
Conference name2018 IEEE/ACM 11th International Conference on Utility and Cloud Computing (UCC)uulm.conferenceName
Conference placeZurichuulm.conferencePlace
Conference start date2018-12-17uulm.conferenceStartDate
Conference end date2018-12-20uulm.conferenceEndDate
Bibliographyuulmuulm.bibliographie


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