Data variety is one of the most important features of Big Data. Data variety is the result of aggregating data from multiple sources and uneven distribution of data. This feature of Big Data causes high variation in the consumption of processing resources such as CPU consumption. This issue has been overlooked in previous works. To overcome the mentioned problem, in the present work, we used Dynamic Voltage and Frequency Scaling (DVFS) to reduce the energy consumption of computation. To this goal, we consider two types of deadlines as our constraint. Before applying the DVFS technique to computer nodes, we estimate the processing time and the frequency needed to meet the deadline. In the evaluation phase, we have used a set of data sets and applications. The experimental results show that our proposed approach surpasses the other scenarios in processing real datasets. Based on the experimental results in this paper, DV-DVFS can achieve up to 15% improvement in energy consumption.

数据多样性是大数据最重要的功能之一。数据多样性是聚合来自多个来源的数据以及数据分布不均的结果。大数据的此功能会导致处理资源消耗（例如CPU消耗）变化很大。这个问题在以前的作品中被忽略了。为了克服上述问题，在当前工作中，我们使用了动态电压和频率缩放（DVFS）来减少计算的能耗。为此，我们将两种类型的截止日期视为约束。在将DVFS技术应用于计算机节点之前，我们估计了处理时间和满足截止日期所需的频率。在评估阶段，我们使用了一组数据集和应用程序。实验结果表明，我们提出的方法在处理实际数据集方面优于其他方案。根据本文的实验结果，DV-DVFS可以将能耗降低多达15％。