Statistical Analysis and Modeling of Heterogeneous Workloads on Amazon's Public Cloud Infrastructure
- Frederick Nwanganga, University of Notre Dame, Notre Dame, Indiana, United States
- Nitesh V Chawla, Department of Computer Science and Engineering, University of Notre Dame, South Bend, Indiana, United States
- Gregory Madey, Computer Science & Engineering, University of Notre Dame, Notre Dame, Indiana, United States
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.
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