mlsanityData to 14 Oct 2026

A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration

arXiv 2404.12498 ↗First posted 18 Apr 2024cs.LG cs.AI cs.SYcited by 1

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Abstract (arXiv)

There have been growing discussions on estimating and subsequently reducing the operational carbon footprint of enterprise data centers. The design and intelligent control for data centers have an important impact on data center carbon footprint. In this paper, we showcase PyDCM, a Python library that enables extremely fast prototyping of data center design and applies reinforcement learning-enabled control with the purpose of evaluating key sustainability metrics including carbon footprint, energy consumption, and observing temperature hotspots. We demonstrate these capabilities of PyDCM and compare them to existing works in EnergyPlus for modeling data centers. PyDCM can also be used as a standalone Gymnasium environment for demonstrating sustainability-focused data center control.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 5 of its 24 references that are in the map. First posted on arXiv 18 Apr 2024.

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5 of 24 references.

References · 5

  1. 2022AI and ML Accelerator Survey and Trendscited by 23
  2. 2018Reinforcement Learning Testbed for Power-Consumption Optimizationcited by 18
  3. 2023RTDK-BO: High Dimensional Bayesian Optimization with Reinforced Transformer Deep kernelscited by 11
  4. 2022Skip Training for Multi-Agent Reinforcement Learning Controller for Industrial Wave Energy Converterscited by 9
  5. 2019ModelicaGym: Applying Reinforcement Learning to Modelica Modelscited by 6

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Citing papers per year: 2024: 1.

1 paper.

  1. 2024SustainDC: Benchmarking for Sustainable Data Center Controlcited by 8

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  1. 2025Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled Data Center Clusters
  2. 2024Carbon Footprint Reduction for Sustainable Data Centers in Real-Time
  3. 2025LC-Opt: Benchmarking Reinforcement Learning and Agentic AI for End-to-End Liquid Cooling Optimization in Data Centers
  4. 2025Green Energy Management for Sustainable Data Centers Using Deep Reinforcement Learning
  5. 2025Data Center Cooling System Optimization Using Offline Reinforcement Learning
  6. 2025DCcluster-Opt: Benchmarking Dynamic Multi-Objective Optimization for Geo-Distributed Data Center Workloads
  7. 2024Game-Theoretic Deep Reinforcement Learning to Minimize Carbon Emissions and Energy Costs for AI Inference Workloads in Geo-Distributed Data Centers
  8. 2024Sustainability of Data Center Digital Twins with Reinforcement Learning