Data Center Digital Twin: Real-Time 3D Visualization and AI-Based Expansion Recommendation
DOI:
https://doi.org/10.5281/zenodo.24011827Keywords:
digital twin, data center, capacity planning, 3D visualization, what-if simulation, energy efficiencyAbstract
The rapid growth of global data continuously pushes data center infrastructure toward its physical limits, yet deciding when and how to expand capacity remains a critical challenge for operators. Sudden traffic surges can exceed the throughput of physical infrastructure, while uncontrolled power consumption and overheating threaten densely packed server racks. This paper presents a data center digital twin that unifies real-time telemetry, interactive 3D visualization, and an AI-based expansion recommendation engine in a single web-based system. The architecture follows an eight-block digital twin design: physical facilities, IoT data acquisition, data integration, the virtual twin state, prediction and explainability, optimization and feedback, the user dashboard, and a closed feedback loop. The twin state is maintained as a structured vector covering power infrastructure, cooling, environment, workload distribution, server utilization, network health, and equipment health. An expansion scoring engine computes a vertical score for specification upgrades and a horizontal score for adding nodes or racks from utilization, traffic, power, and cost inputs, and derives a confidence score from the gap between the two. The 3D scene, rendered with WebGL, animates data flow as dynamic particles and maps temperature and load telemetry onto rack heat colors, while a control panel with sliders and quick scenario presets enables zero-risk what-if simulation. A functional prototype with sixteen racks and one hundred twenty-eight servers demonstrates centralized visual monitoring, proactive risk testing through quick scenario presets, and data-driven expansion recommendations with confidence scores. The main contribution is an integrated digital twin architecture that couples real-time 3D visualization with a transparent scoring-based expansion decision engine for data center operators.
References
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[2] J. Athavale, C. Bash, W. Brewer, M. Maiterth, D. Milojicic, H. Petty, and S. Sarkar, "Digital Twins for Data Centers," Computer, vol. 57, no. 10, pp. 151-158, 2024. DOI: 10.1109/MC.2024.3436945
[3] A. Fuller, Z. Fan, C. Day, and C. Barlow, "Digital Twin: Enabling Technologies, Challenges and Open Research," IEEE Access, vol. 8, pp. 108952-108971, 2020. DOI: 10.1109/ACCESS.2020.2998358
[4] W. Brewer, M. Maiterth, V. Kumar, R. Wojda, S. Bouknight, J. Hines, W. Shin, S. Greenwood, D. Grant, W. Williams, and F. Wang, "A Digital Twin Framework for Liquid-Cooled Supercomputers as Demonstrated at Exascale," in Proc. Int. Conf. for High Performance Computing, Networking, Storage and Analysis (SC '24), Atlanta, GA, USA, 2024, pp. 1-18. DOI: 10.1109/SC41406.2024.00029
[5] T. I. Zohdi, "A Digital-Twin and Machine-Learning Framework for Precise Heat and Energy Management of Data-Centers," Computational Mechanics, vol. 69, no. 6, pp. 1501-1516, 2022. DOI: 10.1007/s00466-022-02152-3
[6] Z. Zhang, Y. Zeng, H. Liu, C. Zhao, F. Wang, and Y. Chen, "Smart DC: An AI and Digital Twin-Based Energy-Saving Solution for Data Centers," in Proc. NOMS 2022 - IEEE/IFIP Network Operations and Management Symposium, Budapest, Hungary, 2022, pp. 1-6. DOI: 10.1109/NOMS54207.2022.9789853
[7] H. Hong, Q. Wu, F. Dong, W. Song, R. Sun, T. Han, C. Zhou, and H. Yang, "NetGraph: An Intelligent Operated Digital Twin Platform for Data Center Networks," in Proc. ACM SIGCOMM 2021 Workshop on Network-Application Integration (NAI '21), Virtual Event, 2021, pp. 26-32. DOI: 10.1145/3472727.3472802
[8] W. Zhang, Y. Wen, Y. W. Wong, K. C. Toh, and C.-H. Chen, "Towards Joint Optimization over ICT and Cooling Systems in Data Centre: A Survey," IEEE Commun. Surv. Tutorials, vol. 18, no. 3, pp. 1596-1616, 2016. DOI: 10.1109/COMST.2016.2545109
[9] Y. Li, C. Zhu, X. Li, and B. Yang, "A Review of Non-Uniform Load Distribution and Solutions in Data Centers: Micro-Scale Liquid Cooling and Large-Scale Air Cooling," Energies, vol. 18, no. 1, 149, 2025. DOI: 10.3390/en18010149
[10] R. Zhao, Y. Du, X. Yang, Z. Zhou, W. Wang, and X. Yang, "A Critical Review on the Thermal Management of Data Center for Local Hotspot Elimination," Energy and Buildings, vol. 297, 113486, 2023. DOI: 10.1016/j.enbuild.2023.113486
[11] Y. Du, Z. Zhou, X. Yang, X. Yang, C. Wang, J. Liu, and J. Yuan, "Dynamic Thermal Environment Management Technologies for Data Center: A Review," Renewable and Sustainable Energy Reviews, vol. 187, 113761, 2023.
[12] S. Xu, H. Zhang, and Z. Wang, "Thermal Management and Energy Consumption in Air, Liquid, and Free Cooling Systems for Data Centers: A Review," Energies, vol. 16, 1279, 2023. DOI: 10.3390/en16031279
[13] E. Cortez, A. Bonde, A. Muzio, M. Russinovich, M. Fontoura, and R. Bianchini, "Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms," in Proc. 26th ACM Symp. Operating Systems Principles (SOSP), Shanghai, China, 2017, 15 pp. DOI: 10.1145/3132747.3132772
[14] R. N. Calheiros, R. Ranjan, A. Beloglazov, C. A. F. De Rose, and R. Buyya, "CloudSim: A Toolkit for Modeling and Simulation of Cloud Computing Environments and Evaluation of Resource Provisioning Algorithms," Software: Practice and Experience, vol. 41, no. 1, pp. 23-50, 2011. DOI: 10.1002/spe.995
[15] D. Kliazovich, P. Bouvry, and S. U. Khan, "GreenCloud: A Packet-level Simulator of Energy-aware Cloud Computing Data Centers," J. Supercomput., vol. 62, no. 3, pp. 1263-1283, 2012. DOI: 10.1007/s11227-010-0504-1
[16] X. Wang, X. Wang, G. Xing, J. Chen, C.-X. Lin, and Y. Chen, "Intelligent Sensor Placement for Hot Server Detection in Data Centers," IEEE Trans. Parallel Distrib. Syst., vol. 24, no. 8, pp. 1577-1588, 2013. DOI: 10.1109/TPDS.2012.254
[17] W.-X. Chu and C.-C. Wang, "CFD Investigation of Airflow Management in a Small Container Data Center," IEEE Trans. Compon. Packag. Manuf. Technol., vol. 9, no. 11, pp. 2177-2188, 2019. DOI: 10.1109/TCPMT.2019.2916017
[18] Y. Ran, H. Hu, Y. Wen, and X. Zhou, "Optimizing Energy Efficiency for Data Center via Parameterized Deep Reinforcement Learning," IEEE Trans. Serv. Comput., vol. 16, no. 2, pp. 1310-1323, 2023. DOI: 10.1109/TSC.2022.3184835
[19] A. Beloglazov, J. Abawajy, and R. Buyya, "Energy-Aware Resource Allocation Heuristics for Efficient Management of Data Centers for Cloud Computing," Future Gener. Comput. Syst., vol. 28, no. 5, pp. 755-768, 2012.
[20] Y. Li, Y. Wen, D. Tao, and K. Guan, "Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning," IEEE Trans. Cybern., vol. 50, no. 5, pp. 2002-2013, 2020. DOI: 10.1109/TCYB.2019.2927410
[21] I. Goiri, W. Katsak, K. Le, T. D. Nguyen, and R. Bianchini, "Parasol and GreenSwitch: Managing Datacenters Powered by Renewable Energy," in Proc. ACM Int. Conf. Architectural Support for Programming Languages and Operating Systems (ASPLOS), Houston, TX, USA, 2013, pp. 51-64.
[22] M. Xu and R. Buyya, "Managing Renewable Energy and Carbon Footprint in Multi-Cloud Computing Environments," J. Parallel Distrib. Comput., vol. 135, pp. 191-202, 2020. DOI: 10.1016/j.jpdc.2019.09.015
[23] M. Avgerinou, P. Bertoldi, and L. Castellazzi, "Trends in Data Centre Energy Consumption under the European Code of Conduct for Data Centre Energy Efficiency," Energies, vol. 10, no. 10, 1470, 2017. DOI: 10.3390/en10101470
[24] M. Azarifar, M. Arik, and J.-Y. Chang, "Liquid Cooling of Data Centers: A Necessity Facing Challenges," Applied Thermal Engineering, vol. 247, 123112, 2024. DOI: 10.1016/j.applthermaleng.2024.123112
[25] R. Wang, X. Zhou, L. Dong, Y. Wen, R. Tan, L. Chen, G. Wang, and F. Zeng, "Kalibre: Knowledge-based Neural Surrogate Model Calibration for Data Center Digital Twins," in Proc. 7th ACM Int. Conf. on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '20), Virtual Event, Japan, 2020, pp. 200-209. DOI: 10.1145/3408308.3427982
[26] Z. Cao, R. Wang, X. Zhou, and Y. Wen, "Reducio: Model Reduction for Data Center Predictive Digital Twins via Physics-Guided Machine Learning," in Proc. 9th ACM Int. Conf. on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '22), Boston, MA, USA, 2022, pp. 1-10. DOI: 10.1145/3563357.3564050
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[28] H. Yuan, Z. Zhang, D. Yang, T. Xue, D. Wen, and G. Yao, "An Intelligent Thermal Management Strategy for a Data Center Prototype Based on Digital Twin Technology," Applied Sciences, vol. 15, art. 7675, 2025. DOI: 10.3390/app15147675
[29] B. Bergeron, M. Hubbell, D. Mojica, A. Reuther, W. Arcand, D. Bestor, D. Burrill, C. Byun, V. Gadepally, M. Houle, H. Jananthan, M. Jones, P. Luszczek, P. Michaleas, L. Milechin, J. Mullen, A. Prout, A. Rosa, C. Yee, and J. Kepner, "Supercomputer 3D Digital Twin for User Focused Real-Time Monitoring," in Proc. IEEE High Performance Extreme Computing Conf. (HPEC), 2024, pp. 1-8.
[30] D. Saxena, J. Kumar, A. K. Singh, and S. Schmid, "Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud," IEEE Trans. Parallel Distrib. Syst., 2023. DOI: 10.1109/TPDS.2023.3240567
[31] D. Saxena and A. K. Singh, "Workload Pattern Learning-Based Cloud Resource Management Models: Concepts and Meta-Analysis," IEEE Trans. Sustainable Comput., vol. 10, no. 3, pp. 418-438, 2025. DOI: 10.1109/TSUSC.2024.3456429
[32] S. Suyahman, D. U. Iswavigra, H. Wibowo, A. B. Trisnawan, A. Wicaksono, et al., "Digital Twin Driven Real Time Performance Optimization of Smart Factory Production Systems Using Edge Computing and Industrial Internet of Things Architecture," Int. J. Industrial Innovation and Mechanical Engineering, vol. 1, no. 2, 2024.
[33] S. Suyahman, D. Prasetyo, A. B. Trisnawan, A. Wicaksono, and M. Furqon, "Explainable Artificial Intelligence Framework for Interpretable Fault Diagnosis and Remaining Useful Life Prediction in Smart Industrial Rotating Machinery," IJMICSE: Int. J. Mechanical, Industrial and Control Systems Engineering, 2024.
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Published
2026-10-30
How to Cite
[1]
M. L. Al-Hakim, M. Prasetyo, and A. Khoirudin, “Data Center Digital Twin: Real-Time 3D Visualization and AI-Based Expansion Recommendation”, KOMPUTER, vol. 1, no. 1, pp. 18–25, Oct. 2026.
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