Fog computing, which distributes computing resources to multiple locations between the Internet of Things (IoT) devices and the cloud, is attracting considerable attention from academia and industry. Yet, despite the excitement about the potential of fog computing, few comprehensive studies quantitatively characterizing the properties of fog computing architectures have been conducted. In this paper we examine the statistical properties of fog computing task completion latencies, which are important to understand to develop algorithms that match IoT nodes’ tasks with the best execution points within the fog computing substrate. Towards characterizing task completion latencies, we developed and deployed a set of benchmarks in 6 different locations, which included local nodes of different grades, conventional cloud computing services in two different regions, and AWS and Microsoft Azure serverless computing options. Using the developed infrastructure, we conducted a series of targeted experiments with a node invoking our benchmarks from different locations and in different conditions. The empirical study elucidated several important properties of task execution latencies, including latency variation across different execution points and execution options, and stability with respect to time. The study also demonstrated important properties of serverless execution options, and showed that statistical structure of computing latencies can be accurately characterized based on a small number (only 10-50) of latency samples. The complete measurement set we have captured as part of this study will be publicly available.

雾计算将计算资源分配到物联网（IoT）设备和云之间的多个位置，正在引起学术界和行业的极大关注。然而，尽管人们对雾计算的潜力感到兴奋，但很少进行定量描述雾计算体系结构特性的综合研究。在本文中，我们检查了雾计算任务完成延迟的统计特性，这对于理解开发将IoT节点的任务与雾计算基质内的最佳执行点匹配的算法非常重要。为了表征任务完成延迟，我们在6个不同的位置开发和部署了一组基准测试，其中包括不同级别的本地节点，两个不同区域中的常规云计算服务以及AWS和Microsoft Azure无服务器计算选项。使用发达的基础架构，我们对一个节点进行了一系列针对性的实验，该节点从不同的位置和不同的条件调用我们的基准测试。经验研究阐明了任务执行延迟的几个重要属性，包括不同执行点和执行选项之间的等待时间变化以及相对于时间的稳定性。该研究还展示了无服务器执行选项的重要属性，并表明可以基于少量（仅10至50个）延迟样本来准确表征计算延迟的统计结构。我们作为这项研究的一部分而捕获的完整度量集将公开提供。