Edge computing has become a promising computing paradigm for building IoT (Internet of Things) applications, particularly for applications with specific constraints such as latency or privacy requirements. Due to resource constraints at the edge, it is important to efficiently utilize all available computing resources to satisfy these constraints. A key challenge in utilizing these computing resources is the scheduling of different computing tasks in a dynamically varying, highly hybrid computing environment. This paper described the design, implementation, and evaluation of a distributed scheduler for the edge that constantly monitors the current state of the computing infrastructure and dynamically schedules various computing tasks to ensure that all application constraints are met. This scheduler has been extensively evaluated with real-world AI applications under different scenarios and demonstrates that it outperforms current scheduling approaches in satisfying various application constraints.

翻译：边缘计算已成为构建物联网应用的一种有前景的计算范式，尤其适用于具有延迟或隐私要求等特定约束的应用。由于边缘端资源有限，高效利用所有可用计算资源以满足这些约束至关重要。利用这些计算资源的一个关键挑战是在动态变化、高度混合的计算环境中调度不同的计算任务。本文描述了一种面向边缘的分布式调度器的设计、实现与评估，该调度器持续监控计算基础设施的当前状态，并动态调度各种计算任务，以确保满足所有应用约束。该调度器已通过真实AI应用在不同场景下进行广泛评估，结果表明其在满足各类应用约束方面优于当前的调度方法。