用强化学习提升多优先级任务调度效率，兼顾高优先级任务完成率。
Improving Mixed-Criticality Scheduling with Reinforcement Learning
- 将调度问题建模为马尔可夫决策过程，用强化学习生成近优解。
- 在10万次模拟中实现80%整体任务完成率，高优先级任务达85%。
- 适合实时与安全关键系统，尤其动态环境下的复杂调度场景。
本文提出一种新的强化学习（RL）方法，用于在变速处理器上调度多优先级（MC）系统。基于已有工作[1]，我们将其扩展至处理非抢占式调度问题，该问题已知为NP难。通过将调度挑战建模为马尔可夫决策过程（MDP），我们开发了一个能够为实时MC系统生成近最优调度的RL代理。该调度器优先保障高优先级任务，同时维持整体系统性能。大量实验表明，该方法具有良好的可扩展性和有效性：在10万例合成数据和真实数据下，平均整体任务完成率达80%，高优先级任务完成率达85%；在稳定无退化条件下，整体完成率可达94%，高优先级任务达93%。结果凸显了基于强化学习的调度器在实时与安全关键应用中的潜力，显著提升了复杂动态调度场景的应对能力。
原文摘要 · Abstract (English)
This paper introduces a novel reinforcement learning (RL) approach to scheduling mixed-criticality (MC) systems on processors with varying speeds. Building upon the foundation laid by [1], we extend their work to address the non-preemptive scheduling problem, which is known to be NP-hard. By modeling this scheduling challenge as a Markov Decision Process (MDP), we develop an RL agent capable of generating near-optimal schedules for real-time MC systems. Our RL-based scheduler prioritizes high-critical tasks while maintaining overall system performance. Through extensive experiments, we demonstrate the scalability and effectiveness of our approach. The RL scheduler significantly improves task completion rates, achieving around 80% overall and 85% for high-criticality tasks across 100,000 instances of synthetic data and real data under varying system conditions. Moreover, under stable conditions without degradation, the scheduler achieves 94% overall task completion and 93% for high-criticality tasks. These results highlight the potential of RL-based schedulers in real-time and safety-critical applications, offering substantial improvements in handling complex and dynamic scheduling scenarios.
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