Towards Zero Downtime: Enhancing Data Center Reliability with AI-Driven Predictive Maintenance and Edge Computing Strategies

This study aimed to investigate the role of artificial intelligence (AI) in predictive data center maintenance practices and strategies. Timely detection of different types of equipment faults and subsequent predictive maintenance can enhance data center availability dramatically and minimize costly outages. The rationale for the study came from the rapid growth of digital services and users, as well as the costly data centers supporting this growth. The costs of a minute (or more) of an unplanned service disruption range from $10,000 to $65,000. The three main Data Center Infrastructure Management (DCIM) components—the common sensors for continuous data acquisition, prevention to power off, and ensuing prediction and solution techniques—are investigated. Multiple other machine learning classification models are suggested to be tested on a similar dataset, in addition to classification, and to quantitatively test a real-life installation in future research. In addition, how these components interact in real-world environments is still not clear and would require advanced statistical analyses, which have not been done in this research. Yet, under the conditions of this study, the results demonstrate how AI elements can provide a reliable solution for enhanced data center DCIM and be applied in deliverable form.