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Paper short abstract

The deployment of digital twins in data centers promises to optimize performance efficiency, yet the recursive, data-driven processes of real-time monitoring, simulation, and visualization require substantial computing power, linking operational gains to market logics.

Paper long abstract

While expanding on a planetary scale to serve the growing digital economy, data centers function as laboratories for continuous improvement of performance efficiency, shaped by economic imperatives and regulatory pressures. The recent rapid escalation of AI workloads has amplified energy consumption and introduced new technical challenges for the design, operation, and management of digital infrastructures. Digital twins, dynamic virtual models of data center facilities that integrate real-time operational data with simulation environments, have emerged as tools for navigating this complexity. The incorporation of machine learning and immersive visualization techniques promises more precise, finely calibrated control over infrastructural processes across the facility life cycle.

Drawing on the development and deployment of digital twins in data centers and interviews with designers and engineers, this paper examines how performance efficiency is recursively produced through ongoing measurement, simulation, and operational adjustment. It situates digital twins within a longer lineage of computational modeling practices in the built environment. Extending the digital twin to encompass the full infrastructure, from servers and cabling to cooling systems, requires substantial computing power to maintain high model granularity and constant information exchange between the physical facility and its virtual representation. Gains in system performance, such as thermal dynamics and energy use, are realized alongside the environmental costs of the data practices themselves. Meanwhile, standardizing equipment, protocols, and data workflows enables providers to unify systems on a common platform, coordinate the supply chain, and leverage operational knowledge as a commercial asset, linking efficiency trade-offs to industrial and market interests.