In recent years, computing workloads have shifted from the cloud to the fog, and IoT devices are becoming powerful enough to run containerized services. While the combination of IoT devices and fog computing has many advantages, such as increased efficiency, reduced network traffic and better end user experience, the scale and volatility of the fog and edge also present new problems for service deployment scheduling.Fog and edge networks contain orders of magnitude more devices than cloud data centers, and they are often less stable and slower. Additionally, frequent changes in network topology and the number of connected devices are the norm in edge networks, rather than the exception as in cloud data centers.This article presents a service scheduling algorithm, labeled “Swirly”, for fog and edge networks containing hundreds of thousands of devices, which is capable of incorporating changes in network conditions and connected devices. The theoretical performance is explored, and a model of the behaviour and limits of fog nodes is constructed. An evaluation of Swirly is performed, showing that it is capable of managing service meshes for at least 300.000 devices in near real-time.

中文翻译：

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雾服务位置的近实时优化，用于响应式边缘计算

近年来，计算工作负载已从云转移到雾中，并且物联网设备变得足够强大，可以运行容器化服务。物联网设备和雾计算的结合具有许多优势，例如提高效率，减少网络流量和更好的最终用户体验，但雾和边缘的规模和易变性也为服务部署调度提出了新的问题。雾和边缘网络包含与云数据中心相比，设备数量要多几个数量级，而且它们的稳定性和速度通常较差。此外，网络拓扑的频繁变化和连接设备的数量是边缘网络的常态，而不是云数据中心的例外。本文提出了一种服务调度算法，标记为“ Swirly”，适用于包含成千上万个设备的雾和边缘网络，这些设备能够整合网络条件和连接的设备的变化。探索了理论性能，并建立了雾节点行为和极限模型。进行了Swirly评估，表明它能够近乎实时地管理至少300.000个设备的服务网格。