Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure
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Date
2019-01-08
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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.
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Service Analytics, Decision Analytics, Mobile Services, and Service Science, Analytics, Cloud, Distribution, Economics, Modeling
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10 pages
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Proceedings of the 52nd Hawaii International Conference on System Sciences
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Attribution-NonCommercial-NoDerivatives 4.0 International
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