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

TL;DR

DataCenterGym is a physics-grounded simulation environment for multi-objective data center scheduling, integrating thermal and power dynamics to improve scheduling performance.

Contribution

The paper introduces DataCenterGym, a reusable simulation environment that models thermal and power effects for data center scheduling research.

Findings

H-MPC scheduling algorithm outperforms baseline schedulers in experiments.

The simulator effectively models thermal dynamics and workload interactions.

H-MPC improves energy efficiency and thermal management in data centers.

Abstract

Modern datacenters schedule heterogeneous workloads across geo-distributed sites with diverse compute capacities, electricity prices, and thermal conditions. Compute utilization, heat generation, cooling demand, and energy consumption are tightly coupled, yet most existing schedulers abstract these effects and treat them independently. We present \textit{DataCenterGym}, a physics-grounded simulation environment for job scheduling in geo-distributed data centers, designed as a reusable testbed for future research. The simulator integrates compute queueing, building thermal dynamics, localized HVAC behavior, and temperature-dependent service degradation within a Gymnasium-compatible interface. We also develop a Hierarchical Model Predictive Control (H-MPC) scheduling algorithm that performs distributed job placement while explicitly accounting for thermal and power dynamics. Through experiments on nominal operation and workload sensitivity, we demonstrate how H-MPC improves scheduling performance relative to baseline schedulers.