# Artificial Intelligence for Energy Optimization in Data Centers

arXiv: https://arxiv.org/abs/2609.03716 · HTML: https://mlsanity.com/paper/2609.03716/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 15 of its 63 references that are in the map.
- First posted on arXiv 3 Sep 2026.

## Builds on (most cited first, 15 of 15)

- [Estimating the Carbon Footprint of BLOOM, a 176B Parameter Language Model](https://mlsanity.com/paper/2211.02001/) (2022)
- [A Systematic Review of Green AI](https://mlsanity.com/paper/2301.11047/) (2023)
- [LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models](https://mlsanity.com/paper/2309.14393/) (2023)
- [Energy Considerations of Large Language Model Inference and Efficiency Optimizations](https://mlsanity.com/paper/2504.17674/) (2025)
- [Carbon-Aware Computing for Datacenters](https://mlsanity.com/paper/2106.11750/) (2021)
- [Holistically Evaluating the Environmental Impact of Creating Language Models](https://mlsanity.com/paper/2503.05804/) (2025)
- [Transforming Cooling Optimization for Green Data Center via Deep Reinforcement Learning](https://mlsanity.com/paper/1709.05077/) (2017)
- [Power Stabilization for AI Training Datacenters](https://mlsanity.com/paper/2508.14318/) (2025)
- [Beyond Efficiency: Scaling AI Sustainably](https://mlsanity.com/paper/2406.05303/) (2024)
- [SustainDC: Benchmarking for Sustainable Data Center Control](https://mlsanity.com/paper/2408.07841/) (2024)
- [Performance Analysis of Machine Learning Centered Workload Prediction Models for Cloud](https://mlsanity.com/paper/2302.02452/) (2023)
- [Empirical Measurements of AI Training Power Demand on a GPU-Accelerated Node](https://mlsanity.com/paper/2412.08602/) (2024)
- [LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers](https://mlsanity.com/paper/2511.00116/) (2025)
- [Spatio-temporal load shifting for truly clean computing](https://mlsanity.com/paper/2405.00036/) (2024)
- [ThirstyFLOPS: Water Footprint Modeling and Analysis Toward Sustainable HPC Systems](https://mlsanity.com/paper/2510.00471/) (2025)

## Abstract (arXiv)

Data centers are increasingly optimized by artificial intelligence and, at the same time, increasingly loaded by it. The literature treats these as two unrelated problems: control studies model workload as an exogenous arrival process, while sustainability studies model infrastructure as a fixed multiplier. We screen roughly 194 papers retrieved through a documented protocol, code 63 of them, and report what the coding shows. Of 28 primary control-oriented studies, 18 are validated in simulation alone and 5 reach physical hardware or a production facility; none account for water withdrawal, and none account for embodied carbon. Reported savings intervals across four technique families overlap almost completely, which means the field cannot presently rank its own methods. Ten recurring gaps are scored for consequence and tractability, and we set out CLEAR-DC, a framework coupling a control-policy branch to a workload-demand branch through an explicit elasticity term, reads out net rather than direct benefit, and emits a schema-conformant record covering energy, carbon, water, embodied share and validation venue. The framework is an architectural and methodological proposal, not a trained system; the contribution we defend empirically is the corpus analysis and the reporting schema derived from it. Coding sheet, derived statistics and all result artifacts: https://github.com/Kimalice/AI-for-Energy-Optimization-in-Data-Centers-Closing-the-Optimizer-Load-Loop