Wong, Pei Kei (2025) Predictive maintenance for server failure in virtual environments. Final Year Project, UTAR.
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Abstract
This project develops a predictive maintenance framework for server failures in virtual environments through a six-stage workflow. Large-scale datasets from Google Cluster, Backblaze HDD, CINECA M100, and Azure VM traces were modeled to establish domainspecific baselines, followed by the design of a unified schema enabling cross-domain integration and transfer learning. Evaluation confirmed CPU stress and thermal load as dominant predictors, with thresholding guided by operational risk. A simulation tool with calibrated mathematical risk models, probabilistic scoring, and interactive dashboards was implemented to address deterministic prediction issues, and a pseudo real-time system was demonstrated using streamed logs. The workflow delivers a complete, simulation-ready predictive maintenance pipeline with both academic rigor and practical deployment value.
| Item Type: | Final Year Project / Dissertation / Thesis (Final Year Project) |
|---|---|
| Subjects: | T Technology > T Technology (General) T Technology > TD Environmental technology. Sanitary engineering |
| Divisions: | Faculty of Information and Communication Technology > Bachelor of Information Technology (Honours) Communications and Networking |
| Depositing User: | ML Main Library |
| Date Deposited: | 28 Dec 2025 18:56 |
| Last Modified: | 28 Dec 2025 18:56 |
| URI: | http://eprints.utar.edu.my/id/eprint/6967 |
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