Fog computing resource scheduling in intelligent factories

doi:10.7523/j.issn.2095-6134.2021.05.015

Abstract

Abstract: With the development of next-generation information technology, many traditional factories have begun to transform into smart factories. How to deal with the massive data in smart factories and improve the production efficiency of the factory is still a serious problem. In this paper, based on the characteristics of smart factories, a fog computing framework for smart factories is proposed, and Kubernetes is used to automate the deployment of containerized smart factory applications.And an improved genetic algorithm based interval division genetic algorithm IDGSA (interval division genetic scheduling arithmetic) is proposed to dispatch and allocate container applications in smart factories. Simulation experiments show that compared with Kubernetes' default scheduling algorithm, IDGSA algorithm can reduce data processing time by 50% and increase the utilization rate of fog computing resources by 60%. Compared with traditional genetic algorithms, it has fewer iterations. In this case, the processing time of the data can be reduced by 7%, and the utilization rate of the fog computing resource can be increased by 9%.

Key words: smart factory, fog computing, container, Kubernetes, resource allocation

CLC Number:

TP393

DAI Zhiming, ZHOU Mingtuo, YANG Yang, LI Jian, LIU Jun. Fog computing resource scheduling in intelligent factories[J]. Journal of University of Chinese Academy of Sciences, 2021, 38(5): 702-711.

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