AI-Enhanced Predictive Maintenance Framework for Modular Data Center Infrastructure: An Automated Firmware Lifecycle Management Approach

Xiaoyi Long

Journal of Sustainability, Policy, and Practice, 2025, vol. 1, issue 2, 19-31

Abstract: Modern data centers face increasing complexity in maintaining modular infrastructure components while ensuring optimal performance and minimal downtime. This paper presents an AI-enhanced predictive maintenance framework specifically designed for modular data center infrastructure with automated firmware lifecycle management capabilities. The proposed framework integrates machine learning algorithms with traditional maintenance protocols to predict potential failures, optimize resource allocation, and automate firmware update processes. Our approach combines temporal pattern recognition, anomaly detection, and intelligent decision-making systems to create a comprehensive maintenance ecosystem. The framework demonstrates significant improvements in mean time between failures (MTBF) by 34.7% and reduces unplanned downtime by 42.3% compared to conventional reactive maintenance approaches. Implementation results from enterprise-level deployments show enhanced operational efficiency and substantial cost reductions in infrastructure management. The system's modular architecture enables seamless integration with existing data center management platforms while maintaining scalability and adaptability to diverse hardware configurations.

Keywords: predictive maintenance; artificial intelligence; data center infrastructure; firmware management (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:dba:jsppaa:v:1:y:2025:i:2:p:19-31

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