Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure

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dc.contributor.authorNwanganga, Frederick
dc.contributor.authorChawla, Nitesh V
dc.contributor.authorMadey, Gregory
dc.date.accessioned2019-01-02T23:57:50Z
dc.date.available2019-01-02T23:57:50Z
dc.date.issued2019-01-08
dc.description.abstractWorkload modeling in public cloud environments is challenging due to reasons such as infrastructure abstraction, workload heterogeneity and a lack of defined metrics for performance modeling. This paper presents an approach that applies statistical methods for distribution analysis, parameter estimation and Goodness-of-Fit (GoF) tests to develop theoretical (estimated) models of heterogeneous workloads on Amazon's public cloud infrastructure using compute, memory and IO resource utilization data.
dc.format.extent10 pages
dc.identifier.doi10.24251/HICSS.2019.226
dc.identifier.isbn978-0-9981331-2-6
dc.identifier.urihttp://hdl.handle.net/10125/59626
dc.language.isoeng
dc.relation.ispartofProceedings of the 52nd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectService Analytics
dc.subjectDecision Analytics, Mobile Services, and Service Science
dc.subjectAnalytics, Cloud, Distribution, Economics, Modeling
dc.titleStatistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure
dc.typeConference Paper
dc.type.dcmiText

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