用深度强化学习优化任务预算，减少实时系统中低优先级任务超时。
Enhancing Adaptive Mixed-Criticality Scheduling with Deep Reinforcement Learning
- 用DQN训练代理动态调整低优先级任务预算
- 实验显示预算超限降低至少50%
- 适合汽车等高安全实时系统开发者
自适应混合关键性（AMC）是一种用于混合关键性硬实时系统的固定优先级可抢占调度算法，虽性能优越，但常因低优先级任务超时而丢弃其作业。本文提出基于深度Q网络的深度强化学习（DRL）方法，在离线训练后运行时动态调整低关键性任务的预算，避免预算超限，同时确保未超时任务不丢失截止期限。通过模拟汽车领域真实工作负载进行评估，结果表明：即使任务预算基于执行时间分布采样设定，该方法仍能将预算超限降低至少50%。据我们所知，这是首次在AMC中应用DRL的研究。
原文摘要 · Abstract (English)
Adaptive Mixed-Criticality (AMC) is a fixed-priority preemptive scheduling algorithm for mixed-criticality hard real-time systems. It dominates many other scheduling algorithms for mixed-criticality systems, but does so at the cost of occasionally dropping jobs of less important/critical tasks, when low-priority jobs overrun their time budgets. In this paper we enhance AMC with a deep reinforcement learning (DRL) approach based on a Deep-Q Network. The DRL agent is trained off-line, and at run-time adjusts the low-criticality budgets of tasks to avoid budget overruns, while ensuring that no job misses its deadline if it does not overrun its budget. We have implemented and evaluated this approach by simulating realistic workloads from the automotive domain. The results show that the agent is able to reduce budget overruns by at least up to 50%, even when the budget of each task is chosen based on sampling the distribution of its execution time. To the best of our knowledge, this is the first use of DRL in AMC reported in the literature.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。