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	\bfseries\Huge Abstract
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In the modern AI economy, the strong computational demand causes datacenters to become complex, diverse facilities.
The sheer volume of CPUs, GPUs, NPUs \etc necessary to satisfy the customers' needs complicates system administration.
Moreover, future warehouses are at an even higher risk of becoming unmanageable, according to the Jevon's Paradox of Computer Systems.
To address this problem, the scientific community has proposed digital twinning as a datacenter management tool.
Digital Twins, which mirror complex objects and processes to provide actionable management improvements, are a novel way to tackle the rising warehouse complexity.
However, Datacenter Digital Twins are still under development, and lack crucial features, such as predictive analytics.
Without predictive maintenance and forecasts, system administrators cannot make well-informed operational decisions.

In this work, we propose to enable predictive analytics for datacenters using digital twinning.
We survey the datacenter digital twinning field, and organize our findings into a system model.
Additionally, we design \mysystem~-- a novel reference architecture for predictive datacenter digital twins, and evaluate it through prototype-based experiments.
Our results indicate \mysystem is capable of reliably differentiating between mild and severe compute failures, and can successfully incorporate a predictive analytics engine to the benefit of datacenter managers.

