LEVERAGING AI AND ML TOREVOLUTIONIZE ENERGY EFFICIENCY INDATA CENTERS
Keywords:
AI Energy Optimization, Data Center Efficiency, Predictive Maintenance, Smart Cooling Management, Workload SchedulingAbstract
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.
References
A. Andrae and T. Edler, "On Global Electricity Usage of Communication Technology: Trends to 2030," Challenges, vol. 6, no. 1, pp. 117-157, 2015. [Online]. Available: https://www.mdpi.com/2078-1547/6/1/117
E. Masanet, A. Shehabi, N. Lei, S. Smith, and J. Koomey, "Recalibrating global data center energy-use estimates," Science, vol. 367, no. 6481, pp. 984-986, 2020. [Online]. Available: https://www.science.org/doi/10.1126/science.aba3758
S. Zhang, Y. Liu, W. Meng, Z. Luo, J. Bu, S. Yang, P. Liang, D. Pei, J. Xu, Y. Zhang, Y. Chen, H. Dong, X. Ye, and J. Wang, "Prefix: Switch failure prediction in datacenter networks," Proceedings of the ACM on Measurement and Analysis of Computing Systems, vol. 2, no. 1, pp. 1-29, 2018. [Online]. Available: https://dl.acm.org/doi/10.1145/3179405
R. Evans and J. Gao, "DeepMind AI Reduces Google Data Centre Cooling Bill by 40%," DeepMind Blog, 2016. [Online]. Available: https://deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-cooling-bill-40
J. Gao, "Machine Learning Applications for Data Center Optimization," Google White Paper, 2014. [Online]. Available: https://static.googleusercontent.com/media/research.google.com/en//pubs/archive/42542.pdf
A. Gamboa, "AI for data center cooling: More than just a hot topic," Google Cloud Blog, 2018. [Online]. Available: https://cloud.google.com/blog/products/ai-machine-learning/ai-for-data-center-cooling-more-than-just-a-hot-topic
M. Tirmazi, A. Barker, N. Deng, M. E. Haque, Z. G. Qin, S. Hand, M. Harchol-Balter, and J. Wilkes, "Borg: the Next Generation," in Proceedings of the Fifteenth European Conference on Computer Systems (EuroSys '20), 2020, pp. 1-14. [Online]. Available: https://dl.acm.org/doi/10.1145/3342195.3387517
C. Delimitrou and C. Kozyrakis, "Paragon: QoS-Aware Scheduling for Heterogeneous Datacenters," in Proceedings of the Eighteenth International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS '13), 2013, pp. 77-88. [Online]. Available: https://dl.acm.org/doi/10.1145/2451116.2451125
D. Amodei and D. Hernandez, "AI and Compute," OpenAI Blog, 2018. [Online]. Available: https://openai.com/blog/ai-and-compute/
B. Smith, "Microsoft will be carbon negative by 2030," Official Microsoft Blog, 2020. [Online]. Available: https://blogs.microsoft.com/blog/2020/01/16/microsoft-will-be-carbon-negative-by-2030/
Z. Liu, et al., "Opportunities and Challenges of AI-Enabled Data Center Energy Optimization," IEEE Transactions on Green Communications and Networking, vol. 5, no. 2, pp. 658-672, 2021. [Online]. Available: https://ieeexplore.ieee.org/document/9366881
R. Evans and J. Gao, "DeepMind AI Reduces Google Data Centre Cooling Bill by 40%," DeepMind Blog, 2016. [Online]. Available: https://deepmind.com/blog/article/deepmind-ai-reduces-google-data-centre-cooling-bill-40
Y. Li, et al., "Intelligent fault diagnosis method for data center chillers using deep learning algorithms," Building and Environment, vol. 160, 106201, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0360132319303890
A. Radovanovic, "Our data centers now work harder when the sun shines and wind blows," Google Blog, 2020. [Online]. Available: https://blog.google/inside-google/infrastructure/data-centers-work-harder-sun-shines-wind-blows/
A. Mohan, et al., "Characterizing the AI/ML Landscape for Efficient Large-Scale Computing," IEEE Transactions on Parallel and Distributed Systems, vol. 33, no. 8, pp. 1833-1848, 2022. [Online]. Available: https://ieeexplore.ieee.org/document/9714826
N. Jones, "How to stop data centres from gobbling up the world's electricity," Nature, vol. 561, pp. 163-166, 2018. [Online]. Available: https://www.nature.com/articles/d41586-018-06610-y
