# SustainDC: Benchmarking for Sustainable Data Center Control

arXiv: https://arxiv.org/abs/2408.07841 · HTML: https://mlsanity.com/paper/2408.07841/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 33 references that are in the map.
- First posted on arXiv 14 Aug 2024.

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?](https://mlsanity.com/paper/2011.09533/) (2020)
- [Heterogeneous-Agent Reinforcement Learning](https://mlsanity.com/paper/2304.09870/) (2023)
- [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)
- [PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability](https://mlsanity.com/paper/2310.03906/) (2023)
- [Carbon Footprint Reduction for Sustainable Data Centers in Real-Time](https://mlsanity.com/paper/2403.14092/) (2024)
- [Function Approximation for Reinforcement Learning Controller for Energy from Spread Waves](https://mlsanity.com/paper/2404.10991/) (2024)
- [A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration](https://mlsanity.com/paper/2404.12498/) (2024)

## Built on by (most cited first, 8 of 8)

- [Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimization](https://mlsanity.com/paper/2604.12290/) (2026)
- [AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance](https://mlsanity.com/paper/2506.03828/) (2025)
- [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)
- [Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Review](https://mlsanity.com/paper/2507.10142/) (2025)
- [OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination](https://mlsanity.com/paper/2605.05519/) (2026)
- [Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systems](https://mlsanity.com/paper/2607.03324/) (2026)
- [Artificial Intelligence for Energy Optimization in Data Centers](https://mlsanity.com/paper/2609.03716/) (2026)

## Abstract (arXiv)

Machine learning has driven an exponential increase in computational demand, leading to massive data centers that consume significant amounts of energy and contribute to climate change. This makes sustainable data center control a priority. In this paper, we introduce SustainDC, a set of Python environments for benchmarking multi-agent reinforcement learning (MARL) algorithms for data centers (DC). SustainDC supports custom DC configurations and tasks such as workload scheduling, cooling optimization, and auxiliary battery management, with multiple agents managing these operations while accounting for the effects of each other. We evaluate various MARL algorithms on SustainDC, showing their performance across diverse DC designs, locations, weather conditions, grid carbon intensity, and workload requirements. Our results highlight significant opportunities for improvement of data center operations using MARL algorithms. Given the increasing use of DC due to AI, SustainDC provides a crucial platform for the development and benchmarking of advanced algorithms essential for achieving sustainable computing and addressing other heterogeneous real-world challenges.