Open Access

ARTICLE

FogQSYM: An Industry 4.0 Analytical Model for Fog Applications

by M. Iyapparaja¹, M. Sathish Kumar¹, S. Siva Rama Krishnan¹, Chiranji Lal Chowdhary¹, Byungun Yoon², Saurabh Singh², Gi Hwan Cho^3,*

1 School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, 632014, India
2 Department of Industrial and System Engineering, Dongguk University, Seoul, Korea
3 Division of Computer Science, Jeonbuk National University, Jeonju, Korea

* Corresponding Author: Gi Hwan Cho. Email:

(This article belongs to the Special Issue: Emerging Trends in Software-Defined Networking for Industry 4.0)

Computers, Materials & Continua 2021, 69(3), 3163-3178. https://doi.org/10.32604/cmc.2021.017302

Received 16 January 2021; Accepted 04 April 2021; Issue published 24 August 2021

Abstract

Industry 4.0 refers to the fourth evolution of technology development, which strives to connect people to various industries in terms of achieving their expected outcomes efficiently. However, resource management in an Industry 4.0 network is very complex and challenging. To manage and provide suitable resources to each service, we propose a FogQSYM (Fog–-Queuing system) model; it is an analytical model for Fog Applications that helps divide the application into several layers, then enables the sharing of the resources in an effective way according to the availability of memory, bandwidth, and network services. It follows the Markovian queuing model that helps identify the service rates of the devices, the availability of the system, and the number of jobs in the Industry 4.0 systems, which helps applications process data with a reasonable response time. An experiment is conducted using a Cloud Analyst simulator with multiple segments of datacenters in a fog application, which shows that the model helps efficiently provide the arrival resources to the appropriate services with a low response time. After implementing the proposed model with different sizes of fog services in Industry 4.0 applications, FogQSYM provides a lower response time than the existing optimized response time model. It should also be noted that the average response time increases when the arrival rate increases.

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