  We propose cloud oracles, 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.
