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

TL;DR

This paper proposes DV-DVFS, a method combining data variety considerations with DVFS to reduce energy consumption in big data processing, achieving up to 15% savings by estimating processing times and adjusting frequencies.

Contribution

It introduces a novel approach that integrates data variety features with DVFS to optimize energy efficiency in big data workloads.

Findings

Achieves up to 15% energy reduction.

Effectively manages resource variation due to data diversity.

Outperforms existing methods in experimental evaluations.

Abstract

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.