EVALUATING THE SCALABILITY OF BIG DATA FRAMEWORKS

被引：1
|
作者：
Sanchez, David [2 ]
Solarte, Oswaldo [2 ]
Bucheli, Victor [2 ]
Ordonez, Hugo [1 ]
机构：
[1] Univ San Buenaventura, Cali, Colombia
[2] Univ Valle, Cali, Colombia
来源：
SCALABLE COMPUTING-PRACTICE AND EXPERIENCE | 2018年 / 19卷 / 03期
关键词：
Scalability; Isoefficiency; Big Data; Hadoop; Spark; MapReduce;
D O I：
10.12694/scpe.v19i3.1402
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
The aim of this paper is to present a method based on the isoefficiency model 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. They were checked experimentally to ensure that the evaluated frameworks are scalable and a sublinear function was obtained. This paper discusses how scalability in big data is governed by a constant of scalability (beta).
引用
收藏
页码：301 / 307
页数：7
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