LEVERAGING AI AND ML TO REVOLUTIONIZE ENERGY EFFICIENCY IN DATA CENTERS

This article explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on energy efficiency in data centers. It examines various areas where these technologies drive significant improvements, including predictive analytics, dynamic cooling management, smart workload scheduling, automated peak shaving, real-time optimization, enhanced maintenance strategies, and holistic system integration. Implementing AI/ML solutions optimizes operational costs and contributes to sustainability efforts by reducing the overall carbon footprint of data center facilities. Through continuous analysis, immediate adjustments, and unified optimization across subsystems, AI-driven approaches achieve substantial energy savings while maintaining or improving performance. The article discusses the current state of AI applications in data centers and looks ahead to potential future developments in this rapidly evolving field.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-08-08
DOI
https://doi.org/10.5281/zenodo.13269832
Citations
1
Primary Topic
Cloud Computing and Resource Management
Type
article
Field-Weighted Citation Impact
0.78
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article

LEVERAGING AI AND ML TO REVOLUTIONIZE ENERGY EFFICIENCY IN DATA CENTERS

1 citations
Zenodo (CERN European Organization for Nuclear Research)
Cloud Computing and Resource Management
0.78
article

LEVERAGING AI AND ML TO REVOLUTIONIZE ENERGY EFFICIENCY IN DATA CENTERS

article en
1 citations

Abstract

This article explores the transformative impact of Artificial Intelligence (AI) and Machine Learning (ML) on energy efficiency in data centers. It examines various areas where these technologies drive significant improvements, including predictive analytics, dynamic cooling management, smart workload scheduling, automated peak shaving, real-time optimization, enhanced maintenance strategies, and holistic system integration. Implementing AI/ML solutions optimizes operational costs and contributes to sustainability efforts by reducing the overall carbon footprint of data center facilities. Through continuous analysis, immediate adjustments, and unified optimization across subsystems, AI-driven approaches achieve substantial energy savings while maintaining or improving performance. The article discusses the current state of AI applications in data centers and looks ahead to potential future developments in this rapidly evolving field.

Zenodo (CERN European Organization for Nuclear Research)
Affordable and clean energy
Openalex Percentile: Top 16%
Cloud Computing and Resource Management
0.78
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LEVERAGING AI AND ML TO REVOLUTIONIZE ENERGY EFFICIENCY IN DATA CENTERS · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS