Evaluating The Scalability of Big Data Frameworks

Evaluating The Scalability of Big Data Frameworks

The aim of this paper is to present a method based on the Isoefficiency for assessing the scalability in big data environments. The programs word count and sort were implemented and compared in Hadoop and Spark. The results confirm that isoefficiency presented a linear growth as the size of the data...

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Veröffentlicht in:Scalable Computing. Practice and Experience 2018-09, Vol.19 (3), p.301-307
Hauptverfasser: Sanchez, David, Solarte, Oswaldo, Bucheli, Victor, Ordonez, Hugo
Format: Artikel
Sprache:eng
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Zusammenfassung:The aim of this paper is to present a method based on the Isoefficiency for assessing the scalability in big data environments. The programs word count and sort were implemented and compared in Hadoop and Spark. The results confirm that isoefficiency presented a linear growth as the size of the data sets was increased. It was experimentally confronted that the evaluated frameworks are scalable and a model of the form Y (s) = β X(s)$ where β ≈[0.47-0.85]
ISSN:1895-1767
1895-1767
DOI:10.12694/scpe.v19i3.1402