Abstract

Nowadays, the fast rate of technological advances, such as cloud computing, has led to the rapid growth of the Data Center (DC) market as well as their power consumption. Therefore, DC power management has become increasingly important. While power forecasting can greatly help DC power management and reduce energy consumption and cost. Power forecasting predicts the potential energy failures or sudden fluctuations in power intake from utility grid. However, it is hard especially when variable renewable energies (RE) as well as free cooling such as air economizers are also used. Geo-distributed DCs face an even harder issue: since in addition to local conditions, the overall status of the entire system of collaborating DCs should also be considered. The conventional approach to forecast power consumption in such complicated cases is to construct a closed form formula for power. This is a tedious task that not only needs expert knowledge of how each single cooling or RE system works, but also needs to be done individually for each DC and repeated all over again for each new DC or change of equipment. One alternative is to use machine learning so as to learn over time how the system consumes power in varying conditions of weather, workload, and internal structure in multiple geo-distributed locations. However, due to the wide range of effective features as well as trade-off between the accuracy and processing overhead, one important issue is to obtain an optimal set of more influential features. In this study, we analyze the correlation among geo-distributed DC power patterns with their weather parameters (based on different DC situations and infrastructure) and extract a set of influential features. Afterward, we apply the obtained features to provide a power consumption forecasting model that predict the power pattern of each collaborating DC in a cloud. Our experimental results show that the proposed prediction model for geo-distributed DCs reaches the accuracy of 87.2%.

中文翻译：

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地理分布数据中心基于学习的功率预测：天气参数分析

摘要

如今，诸如云计算之类的技术进步日新月异，导致数据中心（DC）市场及其功耗迅速增长。因此，直流电源管理变得越来越重要。功率预测可以极大地帮助直流电源管理并降低能耗和成本。电力预测可预测潜在的能源故障或从公用电网获得的电力突然波动。但是，特别是在同时使用可变的可再生能源（RE）以及自然冷却（例如节能器）的情况下，这非常困难。地理分布的区议会面临一个甚至更艰巨的问题：由于除了当地条件外，还应考虑协作区议会整个系统的整体状况。在这种复杂情况下，预测功耗的常规方法是构造功率的封闭式公式。这是一项繁琐的任务，不仅需要有关每个单独的冷却或可再生能源系统如何工作的专业知识，而且还需要针对每个DC分别进行，并针对每个新的DC或更换设备再次进行重复。一种替代方法是使用机器学习，以便随着时间的推移了解系统在多个地理位置分布的天气，工作量和内部结构的变化条件下如何消耗功率。但是，由于有效功能的范围广泛，并且在精度和处理开销之间进行权衡，一个重要的问题是获得一组更具影响力的功能的最佳选择。在这个研究中，我们分析了地理分布的DC功率模式与天气参数之间的相关性（基于不同的DC情况和基础设施），并提取了一组有影响力的特征。之后，我们使用获得的功能来提供功耗预测模型，该模型可预测云中每个协作DC的功率模式。我们的实验结果表明，所提出的地理分布DC预测模型的准确率达到87.2％。