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

DV-DVFS: Merging Data Variety and DVFS Technique to Manage the Energy Consumption of Big Data Processing