In this paper, we present the use of optimization models to evaluate how to best allocate cloud computing resources to minimize cost and time to generate analysis. We look at cloud platform providers Amazon Web Services, Google Cloud and Microsoft Azure on their product offering. We selected 18 machine configuration instances among these providers and analyze the pricing structure of the different configuration. Using a support vector machine analysis written in python, performance data was gathered on these instances to compare time and cost on various data sizes. Using this result, we build models that allow us to select the optimal provider and system configuration to minimize cost and resources based on the users' requirement.
Yim, Victor and Fernandes, Colin
"The Resource Allocation Optimization Problem for Cloud Computing Environments,"
SMU Data Science Review: Vol. 1
, Article 2.
Available at: https://scholar.smu.edu/datasciencereview/vol1/iss3/2
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