  An important goal of modern scheduling systems is to efficiently manage power
usage. In energy-efficient scheduling, the operating system controls the speed
at which a machine is processing jobs with the dual objective of minimizing
energy consumption and optimizing the quality of service cost of the resulting
schedule. Since machine-learned predictions about future requests can often be
learned from historical data, a recent line of work on learning-augmented
algorithms aims to achieve improved performance guarantees by leveraging
predictions. In particular, for energy-efficient scheduling, Bamas et. al.
[BamasMRS20] and Antoniadis et. al. [antoniadis2021novel] designed algorithms
with predictions for the energy minimization with deadlines problem and
achieved an improved competitive ratio when the prediction error is small while
also maintaining worst-case bounds even when the prediction error is
arbitrarily large.
  In this paper, we consider a general setting for energy-efficient scheduling
and provide a flexible learning-augmented algorithmic framework that takes as
input an offline and an online algorithm for the desired energy-efficient
scheduling problem. We show that, when the prediction error is small, this
framework gives improved competitive ratios for many different energy-efficient
scheduling problems, including energy minimization with deadlines, while also
maintaining a bounded competitive ratio regardless of the prediction error.
Finally, we empirically demonstrate that this framework achieves an improved
performance on real and synthetic datasets.
