Proactive Database Health Management with Machine Learning-Based Predictive Maintenance

Authors

  • Sandeep Kumar Dasa   Independent Researcher, USA

Keywords:

Database Health Management, Predictive Maintenance, Machine Learning, Proactive Monitoring, Reactive Maintenance Limitations, Database Reliability, Anomaly Detection, Operational Efficiency, System Resilience, Data-Driven Maintenance

Abstract

This assignment focuses on prediction maintenance using machine learning approaches to ensure the good health of the database. Convention approaches to database management are largely followers where problems crop up in the system, and solutions are sought only then. This causes a lot of issues, such as resource wastage. With the help of machine learning, the idea of predictive maintenance is focused on future breakdowns, enabling executable actions that enhance efficiency and reduce downtime. This study employs predictive analysis by checking database statistics (CPU, memory, response time) and using machine learning algorithms to detect signs of approaching problems. The process involves using models to provide the required accuracy and running a real-time application to evaluate the model's usefulness in preventing database failure. Expected outcomes suggest that predictive maintenance can improve database dependability, minimize unavailability, and decrease maintenance expenses. This approach will provide proactive management rather than reactive and change how the database is maintained to a more assertive and economical method.

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IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2023-02-09

Issue

Volume 9, Issue 1, January-February-2023

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Sandeep Kumar Dasa , " Proactive Database Health Management with Machine Learning-Based Predictive Maintenance" International Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 1, pp.294-302, January-February-2023.