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Adaptive Application Performance Management for Big Data Stream Processing

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dc.contributor.authorEichelberger, Holger
dc.contributor.authorQin, Cui
dc.contributor.authorSchmid, Klaus
dc.contributor.authorNiederée, Claudia
dc.date.accessioned2023-03-13T10:09:53Z
dc.date.available2023-03-13T10:09:53Z
dc.date.issued2015
dc.description.abstractBig data applications with their high-volume and dynamically changing data streams impose new challenges to application performance management. Efficient and effective solutions must balance performance versus result precision and cope with dramatic changes in real-time load and needs without overprovisioning resources. Moreover, a developer should not be burdened too much with addressing performance management issues, so he can focus on the functional perspective of the system For addressing these challenges, we present a novel comprehensive approach, which combines software configuration, model-based development, application performance management and runtime adaptation.en
dc.identifier.pissn0720-8928
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40770
dc.language.isoen
dc.publisherGeselllschaft für Informatik e.V.
dc.relation.ispartofSoftwaretechnik-Trends Band 35, Heft 3
dc.subjectAdaptive performance management
dc.subjectresource adaptation
dc.subjectstream processing
dc.subjectalgorithm families
dc.subjectBig data
dc.subjectQualiMaster.
dc.titleAdaptive Application Performance Management for Big Data Stream Processingen
dc.typeText/Journal Article
gi.citation.publisherPlaceBonn
gi.conference.sessiontitleSonderteil: Proceedings of the Symposium on Software Performance (SSP) 2015, 4. - 6. November 2015, Munich, Germany

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Softwaretechnik-Trends 35(3) - 2015