Fog computing (FC) with a distributed architecture plays an essential role in Internet-of-Things (IoT). This paradigm utilises the processing abilities of Fog devices (FDs) and decreases latency. The large volume of data and its process in IoT can cause network failures. Researchers tend to consider communication reliability to reduce fault effects and achieve high performance. Fault tolerance becomes a necessary matter to enhance the reliability of the Fog. Notably, fault tolerance studies have been performed mostly on the Cloud system. To counter this issue, the authors propose a novel fault-tolerant scheduling algorithm of modules in FC and optimise it. The main idea of this approach is a classification method for different modules alongside of computing the energy consumption of all FDs and finding minimal FDs' energy consumption. To distribute modules between FDs, they present an energy-efficient checkpointing and load balancing technique based on the Bayesian classification and call it by ECLB. The performance of the proposed method is evaluated by comparing it with the state-of-the-art algorithms in terms of delay, energy consumption, execution cost, network usage, and total executed modules. Analysis and simulation results indicate that the authors' methods are efficient and superior to others.

中文翻译：

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基于雾的IoT应用程序中的具有负载平衡调度功能的容错

具有分布式架构的雾计算（FC）在物联网（IoT）中起着至关重要的作用。此范例利用了Fog设备（FD）的处理能力并减少了延迟。物联网中的大量数据及其处理过程可能导致网络故障。研究人员倾向于考虑通信可靠性以减少故障影响并实现高性能。容错成为增强雾的可靠性的必要事项。值得注意的是，容错性研究主要在Cloud系统上进行。为了解决这个问题，作者提出了一种新颖的FC模块容错调度算法并对其进行了优化。这种方法的主要思想是针对不同模块的分类方法，以及计算所有FD的能耗并找到最小FD的能耗的方法。为了在FD之间分配模块，他们提出了一种基于贝叶斯分类的节能检查点和负载平衡技术，并通过ECLB进行称呼。通过与延迟，能耗，执行成本，网络使用和总执行模块方面的最新算法进行比较，评估了该方法的性能。分析和仿真结果表明，作者的方法是有效的并且优于其他方法。