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.
翻译:数据多样性是大数据最重要的特征之一。数据多样性是来自多种来源的数据汇总和数据分布不均的结果。大数据的特点导致处理资源(如CPU消费)的消费差异很大,以前的工作忽视了这个问题。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度缩放(DVFS)来减少计算中的能源消耗。为此,我们认为两类最后期限是我们的限制。在对计算机节点应用DVFS技术之前,我们估计了完成最后期限所需的处理时间和频率。在评价阶段,我们使用了一套数据集和应用。实验结果显示,我们提出的处理实际数据集的方法超过了其他设想。根据本文的实验结果,DV-DVFS可以实现能源消耗15%的改善。