DocumentCode :
3738313
Title :
Hadoop branching: Architectural impacts on energy and performance
Author :
Ivanilton Polato;Denilson Barbosa;Abram Hindle;Fabio Kon
Author_Institution :
Department of Computer Science, Federal University of Technology - Paran?, Campo Mour?o, Brazil
fYear :
2015
Firstpage :
1
Lastpage :
4
Abstract :
Data centers are notorious energy consumers. In fact, studies have shown that for every $1 spent on hardware in the datacenter, $0.50 is spent on powering this hardware over its lifetime. Data centers host real or virtual (i.e., cloud) clusters that often execute large compute jobs using MapReduce, of which Hadoop is a popular implementation. Like other successful open source projects, Hadoop has been maintained and evolved over time with new resource management features being added over time in an effort to improve performance, raising questions as to whether such architectural evolution has achieved its goal, and if so, at what cost. In this work we apply Green Mining to find out that later versions of Hadoop - who exhibit more dynamic resource control - can suffer from serious energy consumption performance regressions.
Keywords :
"Correlation","Energy consumption","Yarn","Measurement","Data mining","Software","Java"
Publisher :
ieee
Conference_Titel :
Green Computing Conference and Sustainable Computing Conference (IGSC), 2015 Sixth International
Type :
conf
DOI :
10.1109/IGCC.2015.7393709
Filename :
7393709
Link To Document :
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