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Abstract

Given the rapid rise in energy demand by data centers and computing systems
in general, it is fundamental to incorporate energy considerations when
designing (scheduling) algorithms. Machine learning can be a useful approach in
practice by predicting the future load of the system based on, for example,
historical data. However, the effectiveness of such an approach highly depends
on the quality of the predictions and can be quite far from optimal when
predictions are sub-par. On the other hand, while providing a worst-case
guarantee, classical online algorithms can be pessimistic for large classes of
inputs arising in practice.
This paper, in the spirit of the new area of machine learning augmented
algorithms, attempts to obtain the best of both worlds for the classical,
deadline based, online speed-scaling problem: Based on the introduction of a
novel prediction setup, we develop algorithms that (i) obtain provably low
energy-consumption in the presence of adequate predictions, and (ii) are robust
against inadequate predictions, and (iii) are smooth, i.e., their performance
gradually degrades as the prediction error increases.