Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/59626

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

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Title:Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure
Authors:Nwanganga, Frederick
Chawla, Nitesh V
Madey, Gregory
Keywords:Service Analytics
Decision Analytics, Mobile Services, and Service Science
Analytics, Cloud, Distribution, Economics, Modeling
Date Issued:08 Jan 2019
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/59626
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.226
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Service Analytics


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