mlsanityData to 14 Oct 2026

SustainDC: Benchmarking for Sustainable Data Center Control

arXiv 2408.07841 ↗First posted 14 Aug 2024cs.LG cs.AI cs.SYcited by 8

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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.

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

10 of 33 references.

References · 10

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 942
  3. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 294
  4. 2023Heterogeneous-Agent Reinforcement Learningcited by 69
  5. 2022Carbon Explorer: A Holistic Approach for Designing Carbon Aware Datacenterscited by 32
  6. 2018Reinforcement Learning Testbed for Power-Consumption Optimizationcited by 18
  7. 2023PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainabilitycited by 6
  8. 2024Carbon Footprint Reduction for Sustainable Data Centers in Real-Timecited by 4
  9. 2024Function Approximation for Reinforcement Learning Controller for Energy from Spread Wavescited by 1
  10. 2024A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integrationcited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 4.

8 papers.

  1. 2026Frontier-Eng: Benchmarking Self-Evolving Agents on Real-World Engineering Tasks with Generative Optimizationcited by 16
  2. 2025AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenancecited by 15
  3. 2025LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centerscited by 3
  4. 2025Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusterscited by 2
  5. 2025Toward Adaptable Multi-Agent Reinforcement Learning: An Assumption-Aware Reviewcited by 1
  6. 2026OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordinationcited by 0
  7. 2026Hierarchical Multi-Agent Reinforcement Learning for Carbon-Aware AI Data Centers in Power Distribution Systemscited by 0
  8. 2026Artificial Intelligence for Energy Optimization in Data Centerscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025DCcluster-Opt: Benchmarking Dynamic Multi-Objective Optimization for Geo-Distributed Data Center Workloads
  2. 2024Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers
  3. 2024Sustainability of Data Center Digital Twins with Reinforcement Learning
  4. 2025Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
  5. 2025Data Center Cooling System Optimization Using Offline Reinforcement Learning
  6. 2026DCoPilot: Generative AI-Empowered Policy Adaptation for Dynamic Data Center Operations
  7. 2026MARLIN: Multi-Agent Game-Theoretic Reinforcement Learning for Sustainable LLM Inference in Cloud Datacenters
  8. 2026Characterizing MARL for Energy Control: A Multi-KPI Benchmark on the CityLearn Environment