Nwanganga, FrederickChawla, Nitesh VMadey, Gregory2019-01-022019-01-022019-01-08978-0-9981331-2-6http://hdl.handle.net/10125/59626Workload 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.10 pagesengAttribution-NonCommercial-NoDerivatives 4.0 InternationalService AnalyticsDecision Analytics, Mobile Services, and Service ScienceAnalytics, Cloud, Distribution, Economics, ModelingConference Paper10.24251/HICSS.2019.226