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A time-predictable fog-integrated cloud framework: One step forward in the deployment of a smart factory

机译:时间可预测的雾集成云框架:智能工厂部署向前迈出了一步

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This paper highlights cloud computing as one of the principal building blocks of a smart factory, providing a huge data storage space and a highly scalable computational capacity. The cloud computing system used in a smart factory should be time-predictable to be able to satisfy hard real-time requirements of various applications existing in manufacturing systems. Interleaving an intermediate computing layer-called fog-between the factory and the cloud data center is a promising solution to deal with latency requirements of hard real-time applications. In this paper, a time-predictable cloud framework is proposed which is able to satisfy end-to-end latency requirements in a smart factory. To propose such an industrial cloud framework, we not only use existing real-time technologies such as Industrial Ethernet and the Real-time XEN hypervisor, but we also discuss unaddressed challenges. Among the unaddressed challenges, the partitioning of a given workload between the fog and the cloud is targeted. Addressing the partitioning problem not only provides a resource provisioning mechanism, but it also gives us a prominent design decision specifying how much computing resource is required to develop the fog platform, and how large should the minimum communication bandwidth be between the fog and the cloud data center.
机译:本文重点介绍云计算是智能工厂的主要构建模块之一,它提供了巨大的数据存储空间和高度可扩展的计算能力。智能工厂中使用的云计算系统应该是可预测时间的,以便能够满足制造系统中存在的各种应用程序的实时要求。在工厂和云数据中心之间插入称为雾的中间计算层是解决硬实时应用程序的延迟要求的有前途的解决方案。本文提出了一种可预测时间的云框架,该框架能够满足智能工厂中端到端的延迟要求。要提出这样的工业云框架,我们不仅使用现有的实时技术(例如工业以太网和实时XEN虚拟机管理程序),而且还讨论了未解决的挑战。在尚未解决的挑战中,有针对性的是在雾和云之间分配给定的工作负载。解决分区问题不仅提供了一种资源供应机制,而且还为我们提供了一个重要的设计决策,该决策指定开发雾平台需要多少计算资源,以及雾和云数据之间的最小通信带宽应该达到多少?中央。

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