DV-DVFS：融合数据多样性和DVFS技术管理大数据处理的能耗
DV-DVFS: Merging Data Variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing
摘要：
数据多样性是大数据最重要的特征之一。数据多样性是由多个来源的数据汇总和数据分布不均匀造成的。大数据的这一特征导致处理资源消耗（如CPU消耗）的高度变化。这个问题在以前的作品中被忽视了。为了克服上述问题，在目前的工作中，我们使用动态电压和频率缩放（DVFS），以减少计算的能量消耗。为了实现这个目标，我们考虑两种类型的最后期限作为我们的约束。在将DVFS技术应用于计算机节点之前，我们估计了满足截止日期所需的处理时间和频率。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，我们提出的方法优于其他方案在处理真实数据集。根据本文的实验结果，DV-DVFS可以实现高达15%的能源消耗的改善。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
