Learning to Optimize Non-Clairvoyant Scheduling via Behavioral Cloning

Large-scale cluster systems must schedule jobs whose processing times are unavailable at decision time, a challenge known as non-clairvoyant scheduling. Learning-augmented scheduling has been applied to this problem, feeding machine-learned predictions of job sizes (processing time) into classical online algorithms. However, it collapses the scheduling decision into a single predicted scalar, and the precision of induced ordering only depends on the prediction error of the processing time. In contrast, behavioral cloning has been shown to be a simple yet effective learning-to-schedule method based on imitation learning. In this paper, we propose a behavioral-cloning framework for non-clairvoyant scheduling in both the single-machine and the parallel-machine settings. Our main contributions are as follows. (1) We directly learn the scheduling policy rather than feeding predicted job sizes into a classical online algorithm, so that the scheduling decision no longer depends on the single scalar of predicted processing time only. (2) We address the variable action space that both settings share an attention-based pointer mechanism, which assigns scores to an arbitrary number of pending jobs at every decision step. (3) We conduct experiments on two practical production workloads: ATLAS and AI Spot TRAces (ASTRA), which we construct from the publicly released trace of a production GPU cluster—spanning 463,279 jobs whose size distribution is far heavier-tailed than ATLAS. The learned policy outperforms the strongest learning-augmented baselines on total completion time, maximum stretch, and makespan.