This paper presents a scheduling model, called decision-driven scheduling, elaborates key optimality results for a fundamental scheduling model, and evaluates new heuristics solving more general versions of the problem. In the context of applications that need control and actuation, the traditional execution model has often been either time-driven or event-driven. In time-driven applications, sensors are sampled periodically, leading to the classical periodic task model. In event-driven applications, sensors are sampled when an event of interest occurs, such as motion-activated cameras, leading to an event-driven task activation model. In contrast, in decision-driven applications, sensors are sampled when a particular decision must be made. We offer a justification for why decision-driven scheduling might be of increasing interest to Internet-of-things applications, and explain why it leads to interesting new scheduling problems (unlike time-driven and event-driven scheduling), including the problems addressed in this paper.

中文翻译：

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决策驱动的调度

本文提出了一种称为决策驱动调度的调度模型，阐述了基本调度模型的关键优化结果，并评估了解决该问题更一般版本的新启发式方法。在需要控制和驱动的应用程序上下文中，传统的执行模型通常是时间驱动或事件驱动。在时间驱动的应用程序中，传感器会定期采样，从而产生经典的周期性任务模型。在事件驱动的应用程序中，当感兴趣的事件发生时对传感器进行采样，例如运动激活的相机，从而形成事件驱动的任务激活模型。相比之下，在决策驱动的应用程序中，当必须做出特定决策时，会对传感器进行采样。