DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling

2026-04-17 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Cloud Computing & IT Infrastructure · Depth: Advanced, quick

Summary

DataCenterGym is a new physics-grounded simulation environment for job scheduling in geo-distributed data centers, designed as a reusable testbed for research. This simulator integrates compute queueing, building thermal dynamics, localized HVAC behavior, and temperature-dependent service degradation within a Gymnasium-compatible interface. It addresses the challenge that most existing schedulers abstract the tightly coupled effects of compute utilization, heat generation, cooling demand, and energy consumption. The authors also developed a Hierarchical Model Predictive Control (H-MPC) scheduling algorithm that performs distributed job placement while explicitly accounting for thermal and power dynamics. Experiments show H-MPC improves scheduling performance compared to baseline schedulers under nominal operation and workload sensitivity.

Key takeaway

For research scientists developing data center scheduling algorithms, DataCenterGym offers a robust, physics-grounded simulation environment to test and validate new approaches. You should consider integrating this Gymnasium-compatible simulator to account for complex thermal and power dynamics, which are often abstracted away in simpler models. This can lead to more realistic performance evaluations and the development of more efficient, resilient scheduling solutions.

Key insights

DataCenterGym simulates geo-distributed data center scheduling with integrated thermal and power dynamics.

Principles

• Thermal and power dynamics are tightly coupled.
• Temperature affects service degradation.

Method

Hierarchical Model Predictive Control (H-MPC) performs distributed job placement, explicitly accounting for thermal and power dynamics.

In practice

• Simulate data center scheduling with thermal effects.
• Evaluate schedulers against physics-grounded models.

Topics

• DataCenterGym
• Multi-Objective Scheduling
• Geo-Distributed Data Centers
• Physics-Grounded Simulation
• Hierarchical Model Predictive Control

Best for: Research Scientist, AI Scientist, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.