AI-Enhanced Datacenter Modernization: Leveraging Predictive Analytics for Energy Efficiency and Fault Tolerance
DOI:
https://doi.org/10.15680/IJCTECE.2025.0802009Keywords:
Predictive Analysis in Data, Energy Efficiency Optimization, Fault Tolerance Mechanisms, Intelligent Infrastructure Management, Machine Learning for Data Center OperationsAbstract
The blistering development of the datacenter infrastructures has increased the energy consumption rate and complexity of its work, and it is necessary to find the innovative solutions to sustainability and reliability of functioning. The present paper examines how artificial intelligence (AI) and predictive analytics can be combined with datacenter systems to modernize them, with the key objectives of improving energy efficiency and fault tolerance. The proposed solution will utilize the latest machine learning models, particle swarm optimization, and real-time monitoring systems to predict possible system failures and dynamically adjust the operation parameters to minimize energy consumption without adversely affecting the service performance]. The study combines the experience of big data analytics, digital twin simulations and management of intelligent infrastructure and suggests a generalized approach to AI-based datacenters modernization. The outcome of simulations shows that energy efficiency has been greatly enhanced with power consumption decreasing by between 15 and 28 percent and fault detection error reducing to over 92 percent, that shows the potential of AI to revolutionize traditional datacenter operations. The results indicate the operational and economic advantages of the incorporation of AI-based predictive analytics, which presents a scalable framework of next-generation datacenter management that is directed at sustainability objectives and organizational resilience. Future perspectives of implementing Internet of Things (IoT) devices, edge processing and adaptive control systems within datacenter performance are also outlined in this study.
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