Resource allocation of industry 4.0 micro-service applications across serverless fog federation
Resource allocation of industry 4.0 micro-service applications across serverless fog federation
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DOI：
10.1016/j.future.2024.01.017
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发表时间：
2024-01-31
影响因子：
7.5
通讯作者：
Salehi，Mohsen Amini
中科院分区：
文献类型：
--
作者：
Hussain，Razin Farhan;Salehi，Mohsen Amini
工业 4.0 革命是通过部署在工业现场（生成数据的地方）的无服务器边缘（又名雾）计算平台上的基于人工智能的应用程序（例如用于自动化和维护）实现的。然而，在不确定、容易发生灾害且无法访问云的偏远工业站点（例如海上油田）中，资源有限的雾系统要满足工业 4.0 应用程序的容错和实时约束是具有挑战性的。我们的研究旨在解决这一挑战。我们考虑了雾系统的无弹性本质、工业应用程序的软件架构（基于微服务与整体）以及远程站点人类专家的稀缺。为了实现类似云的弹性，我们的方法是动态地、无缝地（即无需人工干预）联合附近的雾系统。然后，我们开发无服务器资源分配解决方案，该解决方案了解应用程序的软件架构、延迟要求以及底层基础设施的分布式性质。我们提出了在联邦雾中无缝且最佳地划分基于微服务的应用程序的方法。我们的实验评估表明，不仅以无服务器方式克服了弹性，而且我们开发的应用程序分区方法可以比文献中现有的方法按时多服务大约 20% 的任务。
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.