A Configurable Pythonic Data Center Model for Sustainable Cooling and ML Integration
This work addresses sustainability challenges for enterprise data centers by providing a configurable tool for design and control, though it is incremental as it builds on existing modeling approaches.
The paper tackles the problem of reducing the operational carbon footprint of data centers by introducing PyDCM, a Python library for fast prototyping of data center design and reinforcement learning control, which evaluates sustainability metrics like carbon footprint and energy consumption, demonstrating capabilities compared to existing tools like EnergyPlus.
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.