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

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Pith's one-line read DataCenterGym is a Gymnasium-compatible simulator integrating compute queueing, building thermal dynamics, localized HVAC, and temperature-dependent degradation for multi-objective geo-distributed data center scheduling, demonstrated with an H-MPC algorithm that outperforms baselines.

desk verdict DataCenterGym bundles compute, thermal, and power models into one Gym testbed and pairs it with H-MPC, but all gains are shown only inside the simulator. read the letter →

arxiv 2604.15594 v1 pith:LOM3BUMD submitted 2026-04-17 cs.DC cs.AI

Nilavra Pathak , Samadrita Biswas , Nirmalya Roy This is my paper · ORCID
classification cs.DCcs.AI
keywords schedulingcomputethermaldatadatacentergymdynamicsgeo-distributedh-mpc
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Data centers are large facilities packed with servers that run everything from web searches to AI training. Deciding which server handles which task is complex because moving work changes how much power is drawn, how much heat is produced, and how hard the cooling systems must work. These factors interact, yet many existing schedulers treat them in isolation. DataCenterGym creates a single simulation that links job queues, building heat flow, local air conditioning responses, and how high temperatures slow down servers. It uses the standard Gymnasium interface so researchers can plug in their own scheduling algorithms and test them under realistic conditions. The authors also built a hierarchical model predictive control method that plans job placements while looking ahead at thermal and power consequences. Tests on normal operation and changing workloads show this method beats simpler baseline approaches.
Extended reading notes

Core claim

We present DataCenterGym, a physics-grounded simulation environment for job scheduling in geo-distributed data centers... 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.

Load-bearing premise

The integrated models of compute queueing, building thermal dynamics, localized HVAC behavior, and temperature-dependent service degradation are sufficiently accurate representations of real geo-distributed data center physics to make simulation results transferable to practice.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Circularity Check

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No circularity in derivation or prediction chain

full rationale

The paper introduces DataCenterGym as a new Gymnasium-compatible simulator that integrates standard literature models for compute queueing, thermal dynamics, HVAC, and temperature-dependent degradation, plus a new H-MPC algorithm. No equations, first-principles derivations, or predictions are shown that reduce by construction to fitted parameters, self-definitions, or self-citation chains. Performance claims are simulator-internal comparisons under nominal and sensitivity workloads; the contribution is the reusable testbed and algorithm, not a tautological result. This is self-contained engineering work with no load-bearing circular steps.

Assumptions & free parameters 0 free parameters · 1 assumptions · 0 invented entities

Based solely on the abstract, the simulator rests on standard domain assumptions about queueing and thermal modeling; no free parameters, invented entities, or ad-hoc axioms are explicitly listed.

assumptions (1)
  • domain assumption Standard models of compute queueing, building thermal dynamics, localized HVAC, and temperature-dependent service degradation are adequate for the simulation.
    Invoked implicitly by the description of the integrated simulator.

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Pith. "Pith review of DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling." pith.science (2026). https://pith.science/paper/LOM3BUMD

@misc{pith2026260415594,
  author       = {Pith},
  title        = {Pith review of: DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/LOM3BUMD}},
  note         = {Machine review of arXiv:2604.15594}
}
read the original 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.

Figures

Figures reproduced from arXiv: 2604.15594 by the authors.

Figure 1
Figure 1. Closed-loop interaction in DataCenterGym. The scheduler observes the system state, selects job assignment and cooling actions, and the environ￾ment advances via coupled workload execution, thermal dynamics, and power evolution. A. Problem Formulation We consider an online scheduling problem over C compute clusters distributed across D geo-distributed datacenters. Time is discretized into intervals t = 0, . . . , T w… view at source ↗
Figure 2
Figure 2. Thermal response under increasing workload. H-MPC actively tracks temperature setpoints, maintaining tightly bounded distributions and preserving [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. System saturation under increasing load. Each curve traces operating [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗

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