Advancing Data Center Operations Through AI and Machine Learning : A Comprehensive Analysis of Predictive Maintenance and Resource Optimization - Ashok Mohan Chowdhary Jonnalagadda, (International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 2024)
This article systematically analyzes the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) technologies on modern data center operations. Through an extensive review of implemented cases and empirical data from multiple data centers, the article demonstrates how AI-driven solutions significantly enhance operational efficiency, reduce maintenance costs, and improve infrastructure reliability. The findings indicate that predictive maintenance algorithms achieve a 47% reduction in unexpected equipment failures, while ML-based resource optimization leads to a 31% improvement in resource utilization rates. The article examines integrating deep learning models for real-time energy management, resulting in an average 23% reduction in cooling costs and a 0.15 improvement in Power Usage Effectiveness (PUE). Additionally, the article analyzes the implementation of AI-powered security frameworks, which demonstrated a 92% accuracy rate in anomaly detection and reduced false positives by 76% compared to traditional rule-based systems. The article also presents a novel framework for capacity planning using neural networks, achieving an 89% accuracy in demand forecasting over 12 months. These findings provide valuable insights for data center operators and establish best practices for implementing AI/ML solutions in mission-critical infrastructure environments. The article concludes with recommendations for overcoming integration challenges and a roadmap for future technological adoption.
Keywords: Data Center Operations; Artificial Intelligence Implementation; Predictive Maintenance Systems; Machine Learning Infrastructure; Energy Optimization Analytics