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

TL;DR

This paper introduces PyDCM, a Python library for rapid data center design prototyping and reinforcement learning-based control, aimed at optimizing sustainability metrics like carbon footprint and energy use.

Contribution

PyDCM provides a flexible, fast, and Pythonic tool for modeling and controlling data centers, integrating reinforcement learning for sustainability optimization.

Findings

PyDCM enables fast prototyping of data center designs.

It effectively models and controls data center cooling and energy use.

Comparison shows PyDCM's capabilities are comparable or superior to EnergyPlus.

Abstract

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.