The Industry 4.0 revolution has been made possible via AI-based applications (e.g., for automation and maintenance) deployed on the serverless edge (aka fog) computing platforms at the industrial sites -- where the data is generated. Nevertheless, fulfilling the fault-intolerant and real-time constraints of Industry 4.0 applications on resource-limited fog systems in remote industrial sites (e.g., offshore oil fields) that are uncertain, disaster-prone, and have no cloud access is challenging. It is this challenge that our research aims at addressing. We consider the inelastic nature of the fog systems, software architecture of the industrial applications (micro-service-based versus monolithic), and scarcity of human experts in remote sites. To enable cloud-like elasticity, our approach is to dynamically and seamlessly (i.e., without human intervention) federate nearby fog systems. Then, we develop serverless resource allocation solutions that are cognizant of the applications' software architecture, their latency requirements, and distributed nature of the underlying infrastructure. We propose methods to seamlessly and optimally partition micro-service-based application across the federated fog. Our experimental evaluation express that not only the elasticity is overcome in a serverless manner, but also our developed application partitioning method can serve around 20% more tasks on-time than the existing methods in the literature.

翻译：工业4.0革命通过部署在工业现场（数据生成地）的无服务器边缘（即雾）计算平台上的AI应用（例如用于自动化和维护）得以实现。然而，在偏远工业现场（如海上油田）资源受限的雾系统上满足工业4.0应用对容错和实时的约束极具挑战性——这些系统面临不确定性、易受灾害影响且无法接入云。我们的研究正是针对这一挑战。我们考虑了雾系统的非弹性特性、工业应用的软件架构（基于微服务与单体架构），以及偏远现场缺乏人类专家的问题。为实现类云弹性，我们的方法是动态且无缝地（即无需人工干预）联合邻近的雾系统。进而，我们开发了感知应用软件架构、延迟需求及底层基础设施分布式特性的无服务器资源分配方案。我们提出了在联邦雾中实现微服务应用无缝最优划分的方法。实验评估表明，不仅以无服务器方式克服了弹性限制，而且我们提出的应用划分方法比现有文献方法可多按时服务约20%的任务。