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Rathna Kumar, Bhavna Jasmine Maria (2024) Using Machine Learning in Edge Computing for Optimizing Resource Scheduling. Masters thesis, Dublin, National College of Ireland.
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Abstract
Edge computing is an emerging trend that has faced challenges in the allocation of resources in light of its dynamic and distributed nature with implications on latency, computational power and energy. Central data processing is no longer sufficient for high-real time applications such as IoT, self-driving cars, and smart cities. To address these problems, this project proposes a framework that applies machine learning approaches such as Random Forest, Decision Tree, AdaBoost, and Gradient Boost to improve resource scheduling. The purpose of the project is to establish whether the integration of fusion models and cloud computing can enhance resource management. This project established the most effective fusion model through experimentation and implemented it on AWS to prove that cloud-based systems enhance computation and dependability. This framework directly solves the problem stated in the research question of how to improve the resource scheduling for enhancing edge computing services and how the advanced analytics and cloud infrastructure can be used to improve the performance in several latency critical applications.
| Item Type: | Thesis (Masters) |
|---|---|
| Supervisors: | Name Email Mijumbi, Rashid UNSPECIFIED |
| Subjects: | Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Cloud computing Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning |
| Divisions: | School of Computing > Master of Science in Cloud Computing |
| Depositing User: | Ciara O'Brien |
| Date Deposited: | 04 Jul 2025 10:36 |
| Last Modified: | 04 Jul 2025 10:36 |
| URI: | https://norma.ncirl.ie/id/eprint/8051 |
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