OPTIMIZING DATA CENTER OPERATIONS WITH ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

Abstract

The rapid expansion of data centers has led to increasing operational complexities, energy consumption challenges, and the need for enhanced system reliability. Traditional data center management methods, including manual maintenance, static workload allocation, and rule-based fault detection, have proven inefficient in addressing the dynamic demands of modern cloud infrastructure. This study systematically reviews the role of Artificial Intelligence (AI) and Machine Learning (ML) in optimizing data center operations, focusing on predictive maintenance, resource allocation, fault detection, power management, and commissioning processes. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, this study reviewed 113 high-quality peer-reviewed articles published between 2015 and 2022, collectively cited over 8,500 times. The findings indicate that AI-driven predictive maintenance reduces system downtime by 40% and increases equipment lifespan by 25%, while AI-powered resource allocation improves server utilization by 30% and minimizes energy waste. Furthermore, AI-based fault detection enhances anomaly detection accuracy by 45%, mitigating potential failures and security threats in real-time. In terms of power management, AI-driven energy optimization reduces power consumption by 15% and increases renewable energy integration by 25%, making data centers more sustainable. Additionally, AI-assisted Level 1 (L1) commissioning automation decreases human errors by 50% and accelerates facility readiness by 30%, streamlining infrastructure deployment. The study highlights AI’s superiority over traditional data center management techniques, confirming that AI-based approaches provide greater scalability, efficiency, cost savings, and sustainability.