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 	\bfseries\Huge Abstract
 \end{center}
 In the modern AI economy, the strong computational demand causes datacenters to become complex, diverse facilities.
-According to the Jevon's Paradox of Computer Systems, future warehouses are at an even higher risk of becoming unmanageable, due to the sheer volume of CPUs, GPUs, NPUs \etc necessary to satisfy the customers' needs.
+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, provide 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, mandatory for system administrators to make well-informed operational decisions.
+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.