Recent years have seen fog and edge computing emerge as new paradigms to provide more responsive software services. While both these concepts have numerous advantages in terms of efficiency and user experience by moving computational tasks closer to where they are needed, effective service scheduling requires a different approach in the geographically widespread fog than it does in the cloud. Additionally, fog and edge networks are volatile, and of such a scale that gathering all the required data for a centralized scheduler results in prohibitively high memory use and network traffic. Since the fog is a geographically distributed computational substrate, a suitable solution is to use a decentralized service scheduler, deployed on all nodes, which can monitor and deploy services in its neighbourhood without having to know the entire service topology. This article presents a fully decentralized service scheduler, labeled “SoSwirly”, for fog and edge networks containing hundreds of thousands of devices. It scales service instances as required by the edge, based on available resources and flexibly defined distance metrics. A mathematical model of fog networks is presented, along with a theoretical analysis and an empirical evaluation which indicate that under the right conditions, SoSwirly is highly scalable. It is also explained how to achieve these conditions by carefully selecting configuration parameters. Concretely, only 15 MiB of memory is required on each node, and network traffic in the evaluations is less than 4 Kbps on edge nodes, while 4–6% more service instances are created than by a centralized algorithm.

中文翻译：

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响应边缘计算的自组织雾支持服务

近年来，雾计算和边缘计算成为提供响应更快的软件服务的新范例。虽然这两个概念通过将计算任务移动到更接近它们需要的地方而在效率和用户体验方面具有许多优势，但有效的服务调度在地理分布广泛的雾中需要与在云中不同的方法。此外，雾和边缘网络是不稳定的，其规模如此之大，以至于为集中调度程序收集所有必需的数据会导致内存使用和网络流量过高。由于雾是一个地理分布的计算基板，一个合适的解决方案是使用一个分散的服务调度器，部署在所有节点上，它可以监控和部署其附近的服务，而无需了解整个服务拓扑。本文介绍了一个完全去中心化的服务调度器，标记为“SoSwirly”，用于包含数十万台设备的雾和边缘网络。它根据可用资源和灵活定义的距离度量，根据边缘的需要扩展服务实例。提出了雾网络的数学模型，以及理论分析和经验评估，表明在合适的条件下，SoSwirly 具有高度可扩展性。还解释了如何通过仔细选择配置参数来实现这些条件。具体来说，每个节点只需要 15 MiB 的内存，并且评估中的网络流量在边缘节点上小于 4 Kbps，