This paper presents a system for jointly managing cooling units and workload assignment in modular data centers. The system aims to minimize power consumption while respecting temperature constraints, all in a thermally heterogeneous environment. Unlike traditional cooling controllers, which may over/under cool certain areas in the data center due to the use of a single setpoint, our framework does not have a single setpoint to satisfy. Instead, using a data-driven thermal model, the proposed system generates an optimal temperature map, the required temperature distribution matrix (RTDM), to be used by the controller, eliminating under/over cooling and improving power efficiency. The RTDM is the resulting temperature distribution when jointly considering workload assignment and cooling control. In addition, we propose the use of model predictive control (MPC) to regulate the operational variables of cooling units in a power-efficient fashion to comply with the RTDM. Within each iteration of the MPC loop, an optimization problem involving the thermal model is solved, and the underlying thermal model is updated. To prove the feasibility of the proposed power efficient system, it has been implemented on an actual modular data center in our facilities. Results from the implementation show the potential for considerable power savings compared to other control methods.

中文翻译：

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用于数据中心的数据驱动、多设定点模型预测热控制系统

本文介绍了一种用于在模块化数据中心中联合管理冷却装置和工作负载分配的系统。该系统旨在最大限度地减少功耗，同时遵守温度限制，所有这些都在热异质环境中。与传统的冷却控制器不同，传统的冷却控制器可能会由于使用单一设定点而导致数据中心的某些区域过度冷却/冷却不足，我们的框架没有单一的设定点需要满足。相反，所提出的系统使用数据驱动的热模型生成最佳温度图，即所需的温度分布矩阵 (RTDM)，供控制器使用，从而消除冷却不足/过度冷却并提高电源效率。RTDM 是联合考虑工作负载分配和冷却控制时产生的温度分布。此外，我们建议使用模型预测控制 (MPC) 以节能的方式调节冷却装置的运行变量，以符合 RTDM。在 MPC 循环的每次迭代中，都会解决一个涉及热模型的优化问题，并更新基础热模型。为了证明所提议的节能系统的可行性，它已在我们设施中的实际模块化数据中心上实施。实施结果表明，与其他控制方法相比，它具有显着节能的潜力。为了证明所提议的节能系统的可行性，它已在我们设施中的实际模块化数据中心上实施。实施结果表明，与其他控制方法相比，它具有显着节能的潜力。为了证明所提议的节能系统的可行性，它已在我们设施中的实际模块化数据中心上实施。实施结果表明，与其他控制方法相比，它具有显着节能的潜力。