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

arXiv: https://arxiv.org/abs/2404.12498 · HTML: https://mlsanity.com/paper/2404.12498/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 5 of 5)

- [AI and ML Accelerator Survey and Trends](https://mlsanity.com/paper/2210.04055/) (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)
- [ModelicaGym: Applying Reinforcement Learning to Modelica Models](https://mlsanity.com/paper/1909.08604/) (2019)

## Built on by (most cited first, 1 of 1)

- [SustainDC: Benchmarking for Sustainable Data Center Control](https://mlsanity.com/paper/2408.07841/) (2024)

## 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.