Author

Huanghuang Liang

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Preprint Sep 2026

ETCInfer: An Energy-efficient Thermal-aware Cooling-joint Scheduler for LLM Inference in AI Datacenters

Large language model (LLM) inference in AI datacenters creates a coupled control problem between GPU serving and facility cooling. Raising ambient temperature setpoints can reduce cooling energy and carbon, but also shrinks thermal headroom, induces GPU throttling, and leads to Service-Level-Objective (SLO) violations....

Rui Lu, Rui Ge, Huang-Huang Liang et al. · 0 citations

#edge computing Preprint Sep 2026

HeatCache: Thermal-aware Energy-efficient LLM Inference Scheduling for Chassis-level Liquid Cooling in Sustainable Edge Server Rooms

This paper implements HeatCache atop vLLM and shows that it reduces computing energy by up to 18.0%, decreases thermal-throttle exposure by 81.7%, and maintains SLO violation rates below 0.9% even up to $48~^{\circ}\mathrm{C}$.

Rui Lu, Huanghuang Liang, Kai-Qi Guan et al. · 0 citations