用物理模型模拟数据中心调度，兼顾温控与能耗。
DataCenterGym: A Physics-Grounded Simulator for Multi-Objective Data Center Scheduling
- 基于物理规律建模温控与能耗耦合关系
- 新算法在多目标调度中显著提升性能
- 适合研究绿色计算与智能调度的学者
现代数据中心需在地理分布的站点间调度异构工作负载，这些站点具有不同的计算能力、电价和热环境。计算利用率、热量生成、冷却需求与能耗紧密耦合，但现有调度器常忽略这些关联，将其独立处理。我们提出DataCenterGym，一个面向地理分布数据中心作业调度的物理驱动仿真环境，作为未来研究的可复用测试平台。该仿真器集成计算队列、建筑热力学动态、局部空调行为及温度依赖的服务退化，并提供Gymnasium兼容接口。我们还设计了分层模型预测控制（H-MPC）调度算法，在分布式作业部署中显式考虑热力与电力动态。通过标准运行与工作负载敏感性实验，验证了H-MPC相较基线调度器的性能优势。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。