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Fog computing based cost optimization for university governance.

Qi Tian1, Gao Li2

  • 1Financial Department, Central Conservatory of Music, Beijing, 100031, China. 17113134@bjtu.edu.cn.

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|October 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a cost-effective fog computing architecture for universities, enhancing network performance and resource use. The model optimizes resource allocation and error handling, reducing costs and computational load in online education.

Keywords:
Cloud computingFog computingFuzzy inference systemUniversity networks

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Area of Science:

  • Computer Science
  • Information Technology

Background:

  • Traditional cloud systems incur high costs and delays in distributed computing for online education.
  • University governance faces challenges with computational burdens in on-premise or hybrid cloud environments.

Purpose of the Study:

  • To present a novel fog computing architecture for reducing university governance costs.
  • To enhance network performance and optimize resource utilization in educational institutions.

Main Methods:

  • Developed a low-cost, fog-based model integrating a new cost function for resource allocation optimization.
  • Implemented a fuzzy inference system for intelligent error handling and resource prioritization.
  • Conducted simulations using MATLAB to validate the proposed solution.

Main Results:

  • Demonstrated significant cost minimization in university networks.
  • Achieved effective resource optimization through the proposed fog computing architecture.
  • Validated the model's ability to alleviate computational burdens.

Conclusions:

  • The fog-based model effectively reduces costs and improves resource utilization in university governance.
  • This approach addresses shortcomings of traditional cloud systems in distributed online education.
  • The study provides a foundation for future research in optimizing administrative processes and reducing costs in educational institutions.