# Carbon Footprint Reduction for Sustainable Data Centers in Real-Time

arXiv: https://arxiv.org/abs/2403.14092 · HTML: https://mlsanity.com/paper/2403.14092/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 47 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments](https://mlsanity.com/paper/1706.02275/) (2017)
- [Tune: A Research Platform for Distributed Model Selection and Training](https://mlsanity.com/paper/1807.05118/) (2018)
- [Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?](https://mlsanity.com/paper/2011.09533/) (2020)
- [Carbon-Aware Computing for Datacenters](https://mlsanity.com/paper/2106.11750/) (2021)
- [Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenters](https://mlsanity.com/paper/2201.10036/) (2022)
- [Reinforcement Learning Testbed for Power-Consumption Optimization](https://mlsanity.com/paper/1808.10427/) (2018)
- [RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernels](https://mlsanity.com/paper/2310.03912/) (2023)
- [Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converters](https://mlsanity.com/paper/2209.05656/) (2022)
- [Benchmark Generation Framework with Customizable Distortions for Image Classifier Robustness](https://mlsanity.com/paper/2310.18626/) (2023)
- [PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability](https://mlsanity.com/paper/2310.03906/) (2023)
- [Characterizing Co-located Datacenter Workloads: An Alibaba Case Study](https://mlsanity.com/paper/1808.02919/) (2018)

## Built on by (most cited first, 4 of 4)

- [SustainDC: Benchmarking for Sustainable Data Center Control](https://mlsanity.com/paper/2408.07841/) (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)
- [Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters](https://mlsanity.com/paper/2502.08337/) (2025)
- [Fast 3D Surrogate Modeling for Data Center Thermal Management](https://mlsanity.com/paper/2511.11722/) (2025)

## Abstract (arXiv)

As machine learning workloads significantly increase energy consumption, sustainable data centers with low carbon emissions are becoming a top priority for governments and corporations worldwide. This requires a paradigm shift in optimizing power consumption in cooling and IT loads, shifting flexible loads based on the availability of renewable energy in the power grid, and leveraging battery storage from the uninterrupted power supply in data centers, using collaborative agents. The complex association between these optimization strategies and their dependencies on variable external factors like weather and the power grid carbon intensity makes this a hard problem. Currently, a real-time controller to optimize all these goals simultaneously in a dynamic real-world setting is lacking. We propose a Data Center Carbon Footprint Reduction (DC-CFR) multi-agent Reinforcement Learning (MARL) framework that optimizes data centers for the multiple objectives of carbon footprint reduction, energy consumption, and energy cost. The results show that the DC-CFR MARL agents effectively resolved the complex interdependencies in optimizing cooling, load shifting, and energy storage in real-time for various locations under real-world dynamic weather and grid carbon intensity conditions. DC-CFR significantly outperformed the industry standard ASHRAE controller with a considerable reduction in carbon emissions (14.5%), energy usage (14.4%), and energy cost (13.7%) when evaluated over one year across multiple geographical regions.