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

dc.contributor.author Nwanganga, Frederick
dc.contributor.author Chawla, Nitesh V
dc.contributor.author Madey, Gregory
dc.date.accessioned 2019-01-02T23:57:50Z
dc.date.available 2019-01-02T23:57:50Z
dc.date.issued 2019-01-08
dc.description.abstract Workload 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.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2019.226
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59626
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Service Analytics
dc.subject Decision Analytics, Mobile Services, and Service Science
dc.subject Analytics, Cloud, Distribution, Economics, Modeling
dc.title Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure
dc.type Conference Paper
dc.type.dcmi Text
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