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.

翻译：现代数据中心在跨地理分布站点间调度异构工作负载，这些站点具有不同的计算能力、电价和热环境条件。计算利用率、热量产生、冷却需求和能源消耗紧密耦合，但现有的大多数调度器将这些效应抽象化并独立处理。我们提出\textit{DataCenterGym}——一个面向地理分布式数据中心作业调度的物理仿真环境，旨在成为未来研究的可复用测试平台。该仿真器集成了计算排队、建筑热力学、局部暖通空调行为以及温度相关的服务降级，并提供符合Gymnasium规范的交互接口。我们还开发了一种分层模型预测控制（H-MPC）调度算法，该算法在显式考虑热力学和功率动力学的同时执行分布式作业放置。通过在标称运行条件下的实验和负载敏感性分析，我们展示了H-MPC相对于基线调度器的性能提升。