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Simulative Analysis and Performance Evaluation for Data Variety Aware Power Optimization Technique Using Big Data

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Abstract

As advancements in technology domain have promoted with times, so have the amount of data generated. The amount of this data is so volumetric, that mainstream techniques fail to process and analyse the data in an efficient way and hence the requirement of dedicated processing techniques. This gigantic amount and processing of this sort of data often leads to over exploitation of computing resources. This results in a lot of power consumption. Many attempts have been made in this domain at hardware as well as software level and promising results have been achieved, but one issue that has been over looked is the impact of Big Data’s Variety on processing. To address this problem it contributes towards a novel technique which reduces energy consumption on processing of big-data with variety meeting the deadline as Quality of Service. The technique works by reading the whole dataset in chunks and then removing stop words with appropriate algorithm to save processing time. In the evaluation part, a set of datasets of approximately 100 GB aggregated from different sources and evaluated them using benchmark applications. The final outcomes with efficient approach by improving energy consumption and meeting deadline constraints.

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Data Availability

Amazon Datasets—https://nijianmo.github.io/amazon/index.html Guttenberg Data—https://web.eecs.umich.edu/~lahiri/gutenberg_dataset.html Quotes Dataset – https://www.kaggle.com/akmittal/quotes-dataset TPC dataset—https://relational.fit.cvut.cz/dataset/TPCH IMDB dataset—https://datasets.imdbws.com/

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Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.

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  1. Department of Computer Science and Engineering, I K Gujral Punjab Technical University, Kapurthala, Punjab, India

    Raman Kumar

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  1. Raman Kumar
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Kumar, R. Simulative Analysis and Performance Evaluation for Data Variety Aware Power Optimization Technique Using Big Data. Wireless Pers Commun 133, 1987–2002 (2023). https://doi.org/10.1007/s11277-023-10841-2

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