PyDCM: Custom Data Center Models with Reinforcement Learning for Sustainability

TL;DR

PyDCM is a Python-based, scalable, and customizable thermal data center model that leverages reinforcement learning to optimize cooling, significantly improving speed and flexibility over existing models.

Contribution

It introduces PyDCM, a novel thermal modeling platform that enables end-to-end customization and integrates deep reinforcement learning for data center cooling optimization.

Findings

PyDCM is 30 times faster than Energy Plus models.

PyDCM scales sublinearly with the number of CPUs.

The platform supports testing various data center configurations.

Abstract

The increasing global emphasis on sustainability and reducing carbon emissions is pushing governments and corporations to rethink their approach to data center design and operation. Given their high energy consumption and exponentially large computational workloads, data centers are prime candidates for optimizing power consumption, especially in areas such as cooling and IT energy usage. A significant challenge in this pursuit is the lack of a configurable and scalable thermal data center model that offers an end-to-end pipeline. Data centers consist of multiple IT components whose geometric configuration and heat dissipation make thermal modeling difficult. This paper presents PyDCM, a customizable Data Center Model implemented in Python, that allows users to create unique configurations of IT equipment with custom server specifications and geometric arrangements of IT cabinets. The use of vectorized thermal calculations makes PyDCM orders of magnitude faster (30 times) than current Energy Plus modeling implementations and scales sublinearly with the number of CPUs. Also, PyDCM enables the use of Deep Reinforcement Learning via the Gymnasium wrapper to optimize data center cooling and offers a user-friendly platform for testing various data center design prototypes.