This paper proposes efficient strategies that shave Data Centers (DCs)’ monthly peak power demand with the aim of reducing the DCs’ monthly expenses. Specifically, the proposed strategies allow to decide: $i)$i) when and how much of the DC's workload should be delayed given that the workload is made up of multiple classes where each class has a certain delay tolerance and delay cost, and $ii)$ii) when and how much energy should be charged/discharged into DCs’ batteries. We first consider the case where the DC's power demands throughout the whole billing cycle are known and present an optimal peak shaving control strategy for it. We then relax this assumption and propose an efficient control strategy for the case when (accurate/noisy) predictions of the DC's power demands are only known for short durations in the future. Several comparative studies based on real traces from a Google DC are conducted in order to validate the proposed techniques.

中文翻译：

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通过能量存储和工作负载转移控制削减数据中心电力需求峰值

本文提出了削减数据中心 (DC) 每月峰值电力需求的有效策略，目的是降低数据中心的每月开支。具体而言，提议的策略允许决定：$i)$一世) 考虑到工作负载由多个类组成，其中每个类都有一定的延迟容忍度和延迟成本，应何时延迟 DC 的工作负载以及延迟多少，以及 $ii)$一世一世)何时以及应该向 DC 的电池充电/放电多少能量。我们首先考虑直流在整个计费周期内的电力需求已知的情况，并为其提出最佳调峰控制策略。然后，我们放宽了这一假设，并针对 DC 电力需求的（准确/嘈杂）预测仅在未来短时间内已知的情况提出了一种有效的控制策略。进行了多项基于来自 Google DC 的真实轨迹的比较研究，以验证所提出的技术。