Fault‐tolerant with load balancing scheduling in a fog‐based IoT application

Abstract

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.