Learning-augmented algorithms have emerged as a powerful paradigm to surpass traditional worst-case lower bounds by integrating potentially noisy predictions. While this framework has seen success in online scheduling, existing work primarily optimizes job latency while relying on frequent, ``blind'' preemptions. This ignores the fundamental trade-off between algorithmic performance and preemption complexity. We provide the first systematic study of learning-augmented scheduling that curbs preemption while optimizing latency. We establish that the gap between theoretical latency bounds and preemption overhead can be bridged with solid analytical foundations. Our results include $O(1)$-competitive algorithms for single and unrelated parallel machines with only $O(1)$ preemptions per job under accurate predictions, with overhead scaling logarithmically with the prediction error. By providing the first bounded-preemption guarantees for unrelated and malleable machines, we extend the theoretical reach of the learning-augmented framework to more constrained and realistic settings. Finally, our algorithms are validated through experiments.

翻译：学习增强算法已成为一种强大的范式，通过整合可能带噪声的预测来超越传统的悲观最坏情况界。尽管该框架在在线调度领域已取得一定成功，但现有工作主要关注优化作业延迟，且依赖于频繁的“盲目”抢占。这忽略了算法性能与抢占复杂度之间的根本权衡。我们首次系统地研究了在限制抢占的同时优化延迟的学习增强调度。我们证明，理论延迟界与抢占开销之间的差距可以通过坚实的分析基础加以弥合。我们的成果包括单机和无关并行机上的$O(1)$竞争比算法，在预测准确时每作业仅需$O(1)$次抢占，且开销随预测误差呈对数增长。通过首次为无关机和可扩展机提供有界抢占保证，我们将学习增强框架的理论适用范围扩展至更具约束性和现实性的场景。最后，我们通过实验验证了所提算法。