Contract scheduling is a widely studied framework for designing real-time systems with interruptible capabilities. Previous work has showed that a prediction on the interruption time can help improve the performance of contract-based systems, however it has relied on a single prediction that is provided by a deterministic oracle. In this work, we introduce and study more general and realistic learning-augmented settings in which the prediction is in the form of a probability distribution, or it is given as a set of multiple possible interruption times. For both prediction settings, we design and analyze schedules which perform optimally if the prediction is accurate, while simultaneously guaranteeing the best worst-case performance if the prediction is adversarial. We also provide evidence that the resulting system is robust to prediction errors in the distributional setting. Last, we present an experimental evaluation that confirms the theoretical findings, and illustrates the performance improvements that can be attained in practice.

合同调度是设计具有可中断功能的实时系统的广泛研究框架。本研究引入并研究更一般和现实的学习增强设置，其中预测以概率分布形式或多个可能的中断时间集合形式给出。我们设计并分析了在预测准确时表现最佳的调度，并同时保证在预测具有敌对性时表现出最佳的最坏情况性能。我们还提供了证据，证明了在分布设置中得到的系统对于预测错误具有鲁棒性。最后，我们进行了实验评估，确认了理论发现，并展示了在实践中可以实现的性能改进。

分布式和多建议合同调度