  As power management has become a primary concern in modern data centers,
computing resources are being scaled dynamically to minimize energy
consumption. We initiate the study of a variant of the classic online speed
scaling problem, in which machine learning predictions about the future can be
integrated naturally. Inspired by recent work on learning-augmented online
algorithms, we propose an algorithm which incorporates predictions in a
black-box manner and outperforms any online algorithm if the accuracy is high,
yet maintains provable guarantees if the prediction is very inaccurate. We
provide both theoretical and experimental evidence to support our claims.
