AI Summary

New research from the University of Michigan suggests that, for data‑processing services, placing high‑paying customers in a “better‑but‑later” queue and handling smaller jobs first can improve overall performance. The study highlights this approach as particularly relevant for artificial‑intelligence workloads, which are increasing demand on data‑center resources and stressing power grids. By prioritizing smaller tasks, the method aims to alleviate capacity bottlenecks that arise from the surge in AI‑driven processing.

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