  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.
