Please use this identifier to cite or link to this item:
https://rda.sliit.lk/handle/123456789/4099
Title: | Evaluation of Infrastructure as Code (IaC) Approaches for Automated Provisioning and Configuring of IoT Devices in Smart Factory Environments |
Authors: | Weerasekara, W. K. N. |
Keywords: | Evaluation Infrastructure Code (IaC) Approaches Automated Provisioning Configuring IoT Devices Smart Factory Environments |
Issue Date: | Dec-2024 |
Publisher: | SLIIT |
Abstract: | The Internet of Things (IoT) area is gaining with time and predictions are showing that Industrial IoT (IIoT) will gain more and more in the future. Here this research was done to find out the best Infrastructure as Code (IaC) tool from model-driven, Terraform and code-centric Ansible for automatic configuring and provisioning IoT devices in large-scale IIoT systems such as automated factory environments. This research has shown the use of IaC within IIoT to automatically provision and configure components of the IIoT system alongside improving productivity, less human involvement in provisioning and configuring components, minimising the errors in device provisioning, costeffectiveness with increased portability and maintainability of the large scale IIoT system with the benefit of the IaC. Furthermore, the research assessed Terraform and Ansible by analysing the elapsed time, resource utilisation, scalability, and error rate, in provisioning and configuring as well as reconfiguring using a prototyped simulated environment for a factory. Also, the research is contributing to the design and development of a cross-platform IaC script generation and execution application including the monitoring capabilities. This tool is named “KFactory Device Provisioner and Configurator”. This application allows to generation of IaC provisioning scripts and executes those with monitoring capabilities as users’ need via a Graphical User Interface (GUI). The tool also has a system monitoring tool that is very helpful to view the variation of CPU usage, Memory usage and inbound-outbound Network usages in a GUI. Furthermore, the tool also collects the provision data to create a Machine Learning (ML) model to predict and show the expected provisioning time, reconfiguring time, CPU, Memory and Network inbound and outbound usages according to the scale of the provisioning tasks based on the host system’s capabilities. Moreover, with the conclusion of this research, the researchers are encouraged to come up with a fine-tuned, production-grade IaC solution for automatic provisioning and configuring IoT devices in large-scale IIoT systems with reduced deployment time, optimized resource utilizations, having scalability, having low error rate, and reduced reconfiguring time. |
URI: | https://rda.sliit.lk/handle/123456789/4099 |
Appears in Collections: | MSc 2024 |
Files in This Item:
File | Description | Size | Format | |
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MS23046566-Evaluation of Infrastructure as Code (IaC) Approaches for Automated Provisioning and Configuring of IoT Devices 1-17.pdf | 399.12 kB | Adobe PDF | View/Open | |
MS23046566-Evaluation of Infrastructure as Code (IaC) Approaches for Automated Provisioning and Configuring of IoT Devices.pdf Until 2050-12-31 | 4.97 MB | Adobe PDF | View/Open Request a copy |
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