Digital Twin-Driven Intelligent Optimization for Complex AI Computing Infrastructure

Deirbhile Connelly; Sienna L. Healey

Authors

Deirbhile Connelly Department of Computer Science, University of North Texas, Denton, TX, USA. Author
Sienna L. Healey Department of Computer Science and Engineering, University at Buffalo, Buffalo, NY, USA. Author

Keywords:

digital twin, AI computing infrastructure, intelligent optimization, systems architecture, sustainability, governance, fairness, robustness, cyber-physical systems, policy

Abstract

Complex artificial intelligence computing infrastructures now combine heterogeneous accelerators, high-bandwidth networks, distributed storage, cooling systems, power constraints, and rapidly changing model-serving workloads. These infrastructures are difficult to optimize because their behavior emerges from tightly coupled technical, economic, environmental, and organizational processes. This paper examines digital twin-driven intelligent optimization as a system-level strategy for managing such complexity. A digital twin is treated not merely as a simulation artifact but as a continuously updated computational counterpart that supports observation, prediction, decision support, and closed-loop control. The paper develops an architectural account of digital twins for AI infrastructure, covering instrumentation, data fabrics, modeling layers, optimization engines, governance interfaces, and deployment pathways. It analyzes structural trade-offs between fidelity and overhead, centralization and autonomy, efficiency and resilience, and short-term performance and long-term sustainability. It further considers fairness, accountability, privacy, and policy implications when optimization systems allocate scarce computational resources or shape access to AI services. Through cross-domain comparisons with manufacturing, cyber-physical systems, cloud data centers, and smart urban infrastructures, the paper identifies governance and robustness challenges that cannot be solved by predictive accuracy alone. The conclusion argues that digital twin-driven optimization should be designed as a socio-technical capability, not only as a control mechanism, and that its legitimacy depends on transparency, contestability, environmental responsibility, and institutional accountability.

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Digital Twin-Driven Intelligent Optimization for Complex AI Computing Infrastructure

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