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On Learning Parametric Dependencies from Monitoring Data

dc.contributor.authorGrohmann, Johannes
dc.contributor.authorEismann, Simon
dc.contributor.authorKounev, Samuel
dc.contributor.editorKelter, Udo
dc.date.accessioned2023-02-27T13:59:28Z
dc.date.available2023-02-27T13:59:28Z
dc.date.issued2019
dc.description.abstractA common approach to predict system performance are so-called architectural performance models. In these models, parametric dependencies describe the relation between the input parameters of a component and its performance properties and therefore significantly increase the model expressiveness. However, manually modeling parametric dependencies is often infeasible in practice. Existing automated extraction approaches require either application source code or dedicated performance tests, which are not always available. We therefore introduced one approach for identification and one for characterization of parametric dependencies, solely based on run-time monitoring data. In this paper, we propose our idea on combining both techniques in order to create a holistic approach for the identification and characterization of parametric dependencies. Furthermore, we discuss challenges we are currently facing and potential ideas on how to overcome them.en
dc.identifier.pissn0720-8928
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/40492
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftwaretechnik-Trends Band 39, Heft 4
dc.relation.ispartofseriesSoftwaretechnik-Trends
dc.subjectarchitectural performance model
dc.subjectrun-time monitoring
dc.titleOn Learning Parametric Dependencies from Monitoring Dataen
dc.typeText/Conference Paper
gi.citation.endPage16
gi.citation.publisherPlaceBonn
gi.citation.startPage14
gi.conference.date5.-6. November 2019
gi.conference.locationWürzburg
gi.conference.sessiontitle10th Symposium on Software Performance (SSP)

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