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Optimizing the cloud? Don’t train models. Build oracles!

Authors:
Tiemo Bang, Conor Power, Siavash Ameli, Natacha Crooks, Joseph M Hellerstein
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Abstract

We propose cloud oracles as an alternative to machine learning for online optimization of cloud configurations. Our cloud oracle approach guarantees complete accuracy and explainability of decisions for problems that can be formulated as parametric convex optimizations. We give experimental evidence of this technique’s efficacy and share a vision of research directions for expanding its applicability.