Learning-Augmented Online Scheduling with Parsimonious Preemption

2026-05-22

AI Summary

This work addresses the fundamental challenge in online scheduling of leveraging predictive information to improve performance while bounding the number of job preemptions, thereby balancing latency and scheduling overhead. The authors propose a learning-augmented online scheduling algorithm that, for the first time, provides theoretical guarantees on bounded preemptions for both unrelated parallel machines and speed-scalable machine models. By integrating competitive analysis with error-sensitive design, the algorithm achieves an $O(1)$ competitive ratio with only $O(1)$ preemptions per job when predictions are accurate; the number of preemptions grows logarithmically with prediction error, ensuring robustness and low overhead. Experimental results validate the approach’s efficacy and establish an analytical bridge between latency performance and preemption complexity.

Abstract

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.

Problem

• learning-augmented scheduling
• preemption
• online scheduling
• latency optimization
• prediction error

Innovation

• learning-augmented scheduling
• parsimonious preemption
• competitive analysis
• unrelated parallel machines
• malleable jobs