Optimizing Cloud Infrastructure with AI-Powered Predictive Maintenance Solutions
Abstract
Modern cloud platforms have grown to a scale where waiting for a component to fail before acting is no longer a defensible operating model. This paper examines how artificial intelligence is applied to predictive maintenance across cloud infrastructure, where machine learning is used to anticipate hardware and software degradation well before it disrupts service. The approach treats failure as a gradual process rather than a sudden event. Disk drives, memory modules, container hosts, storage volumes, and network elements all tend to show measurable warning signs as they deteriorate, and those signs are already present in the metrics, logs, traces, and hardware sensor readings that cloud systems produce as a matter of course. Supervised learning models, including gradient-boosted decision trees, regression estimators, and recurrent neural networks, are trained on this historical telemetry to recognize degradation signatures and to estimate the remaining useful life of individual components. Production environments reviewed for this study report a failure prediction accuracy of 94.6 percent, an average warning window of 96 hours before failure, and a 73 percent reduction in unplanned downtime relative to threshold-based monitoring. When these prediction pipelines are connected to native cloud monitoring services such as Amazon CloudWatch and Microsoft Azure Monitor, corrective work can be triggered automatically. Reviewed deployments record an automated remediation success rate of 92.7 percent for actions such as workload migration, capacity scaling, and pre-emptive node replacement. The move from a reactive to a proactive posture has measurably improved reliability, with reviewed estates holding service availability at 99.98 percent while cutting maintenance operating cost by roughly 42 percent. These results matter most for workloads where downtime is expensive by the minute, such as payment processing and large-scale e-commerce, and the evidence indicates that AI-powered predictive maintenance has moved from an experimental idea into a practical foundation for operating cloud infrastructure at scale.
Article Information
Journal |
International Journal of Science, Research and Technology |
|---|---|
Volume (Issue) |
Vol. 6 No. 4 (2023): International Journal of Science, Research and Technology (IJSRAT) |
DOI |
|
Pages |
10354-10363 |
Published |
August 16, 2023 |
| Copyright |
All rights reserved |
Open Access |
This work is licensed under a Creative Commons Attribution 4.0 International License. |
How to Cite |
Sai Krishna Gurram (%2023). Optimizing Cloud Infrastructure with AI-Powered Predictive Maintenance Solutions. International Journal of Science, Research and Technology , Vol. 6 No. 4 (2023): International Journal of Science, Research and Technology (IJSRAT) , pp. 10354-10363. https://doi.org/10.15662/IJSRAT.2023.0604009 |
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