To reduce the skyrocketing energy consumption of data centers, the prevailing approaches adopt the time-driven manner to control IT and cooling subsystems. These methods suffer from highly dynamic system states, complex action spaces and the risk of instability caused by frequent and unnecessary control operations. To tackle these problems, we propose a novel event-driven control paradigm and an optimization algorithm, under the deep reinforcement learning (DRL) framework. The principle is to make decisions based on certain critical events (e.g., overheating), rather than fixed periodic control. Specifically, we design an event-driven optimization framework to trigger control operations. Then, we present several models to describe IT and cooling subsystems, and mathematically define events to capture four types of prior factors that impact system performance. Furthermore, we develop an event-driven DRL (E-DRL) optimization algorithm to dispatch jobs and regulate cooling facilities for energy efficiency. Using two different types of real workload traces, we conduct extensive experiments to demonstrate that: 1) E-DRL reduces the number of regulating decisions by 70% $\sim$ 95% while achieving a comparable or even better energy efficiency in comparison with the state-of-the-art algorithm; and 2) E-DRL can adapt the control frequency to the changing operational conditions and diverse workloads.

中文翻译：

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通过事件驱动的深度强化学习优化数据中心能源效率

为降低数据中心暴涨的能耗，目前主流的方法是采用时间驱动的方式来控制 IT 和制冷子系统。这些方法存在高度动态的系统状态、复杂的动作空间以及由频繁和不必要的控制操作引起的不稳定风险。为了解决这些问题，我们在深度强化学习 (DRL) 框架下提出了一种新颖的事件驱动控制范式和优化算法。其原则是根据某些关键事件（如过热）做出决策，而不是固定的周期性控制。具体来说，我们设计了一个事件驱动的优化框架来触发控制操作。然后，我们提出了几个模型来描述 IT 和冷却子系统，并以数学方式定义事件以捕获影响系统性能的四种先验因素。此外，我们开发了一种事件驱动的 DRL (E-DRL) 优化算法来调度作业和调节冷却设施以提高能效。使用两种不同类型的真实工作负载轨迹，我们进行了大量实验来证明：1) E-DRL 将调节决策的数量减少了 70% $\模拟$ 95%，同时实现与最先进算法相当甚至更好的能源效率；2) E-DRL 可以使控制频率适应不断变化的操作条件和多样化的工作负载。