Abstract
In the single-machine non-clairvoyant scheduling problem, the goal is to minimize the total completion time of jobs whose processing times are unknown a priori . We revisit this well-studied problem and consider the question of how to effectively use (possibly erroneous) predictions of the processing times. We study this question from ground zero by first asking what constitutes a good prediction; we then propose a new measure to gauge prediction quality and design scheduling algorithms with strong guarantees under this measure. Our approach to derive a prediction error measure based on natural desiderata could find applications for other online problems.
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