Energy-efficient strategic management of data center with artificial intelligence application

Modern data centers (DCs) face the problem of high energy consumption, which leads
to increased operating costs and negative environmental impact. This raises the need to develop
intelligent control systems (ICS) that can improve energy efficiency and optimize the use of
engineering infrastructure.
The aim of this work is to evaluate the effectiveness of integrating neural networks and a robust
PID controller into an IMS to improve control adaptability, reduce energy consumption and
increase the resilience of data centers to changing operating conditions.
Methods: in order to achieve the set objectives, a simulation model of the TIER IV level data
center was developed in the TRNSYS environment, which allows to simulate dynamic
processes of energy consumption. The model uses machine learning algorithms to predict
thermal load and power consumption, implemented using a neural network trained in MATLAB
environment. A robust PID controller is also implemented to control cooling systems based on
the predicted data. An economic analysis of the efficiency of IMS implementation with the
calculation
 of
 key
 indicators:
 ROI,
 NPV
 and
 BCR.
The novelty of the work consists in proposing an approach to balancing the input data for
neural networks, which allows to reduce the spread of amplitudes of oscillations, reduce the
probability of overtraining and increase the generalization ability of the network. For the first
time, a method of integrating neural networks and robust PID controller for data center energy
management, taking into account dynamic infrastructure changes, has been developed.
Result: The proposed control system reduced the PUE by 3.5%, reduced the power
consumption of the cooling systems to 40% of the total power consumption of the data center
and improved the accuracy of heat load forecasting. This increased the adaptability of the
control and provided a reduction in operating costs.
Practical significance: the developed methods and models are applicable for modernization of
data center engineering infrastructure in order to improve their energy efficiency, reduce costs
and ensure environmental sustainability. The results show the feasibility of implementing IMS
to improve the resilience and adaptability of data centers to changing operating conditions,
which helps to reduce operating costs and environmental impact.

1. Bouaouda A., Afdel K., Abounacer R. Unveiling Genetic Reinforcement Learning (GRLA) and Hybrid Attention-Enhanced Gated Recurrent Unit with Random Forest (HAGRU-RF) for Energy-Efficient Containerized Data Centers Empowered by Solar Energy and AI. Sustainability 2024, 16, 4438.

2. Kruglov V. V., Dli M. I., Golunov R. Yu. Nechetkaya logika i iskusstvennie neironnie seti: uchebnoe posobie dlya vuzov [Fuzzy Logic and Artificial Neural Networks]. Moscow, Fizmatlit, 2001. 496 pages.

3. J. Yang, W. Xiao, C. Jiang, M. S. Hossain, G. Muhammad, S. U. Amin, "AI-Powered Green Cloud and Data Center," in IEEE Access, vol. 7, pp. 4195-4203, 2019, DOI: 10.1109/ACCESS.2018.2888976.

4. Kołodziej J., Khan S.U., Wang L., Byrski A., Min-Allah N., Madani S.A.: Hierarchical genetic-based grid scheduling with energy optimization. *Clust. Comput.* 16(3), 591–609 (2013).

5. Senthilkumar G., Rajendran P., Suresh Y., Herald Anantha Rufus N., Rama Chaithanya Tanguturi, Rajdeep Singh Solanki. Computational Engineering based Approach on Artificial Intelligence and Machine Learning Driven Robust Data Centre for Safe Management. Journal of Machine and Computing, 2023, 3(4): 465-474. DOI: https://doi.org/10.53759/7669/jmc202303038.

6. Majumder D., Kumar S. M. (2021). Optimal and Effective Resource Management in Edge Computing. *Computer Systems Science & Engineering*, 44(2), 1201-1217. DOI: 10.32604/csse.2023.024868.

7. Liu Hui, Aljbri Abdussalam, Song Jie, Jiang Jinqing, Hua Chun. (2022). Research advances on AI-powered thermal management for data centers. Tsinghua Science and Technology. 27. 303-314. DOI:10.26599/TST.2021.9010019.

8. Komar I. A. Theoretical and practical features of data processing centers evaluation (DPCs). Real Estate: Economics, Management. 2023, 2:70-77.

9. Lyashenko M. A. Soderzhanie strategii razvitiya tsentra obrabotki dannikh [Content of the Data Center Development Strategy]. Internet Journal "Naukovedenie", Vol. 7, No. 4, 2015. http://naukovedenie.ru/PDF/85EVN415.pdf (open access). DOI: 10.15862/85EVN415.

10. RBC Capital Markets September 20, 2021 RBC Datacenter Download.

11. Ali Ahmadi. Implementing Artificial Intelligence in IT Management: Opportunities and Challenges. DOI: https://doi.org/10.51983/ajcst-2023.12.2.3704.

12.