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MapReduce performance models for Hadoop 2.x

Author
Glushkova, D.; Jovanovic, P.; Abello, A.
Type of activity
Presentation of work at congresses
Name of edition
19th International Workshop On Design, Optimization, Languages and Analytical Processing of Big Data
Date of publication
2017
Presentation's date
2017-03-21
Book of congress proceedings
Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017): Venice, Italy, March 21-24, 2017
First page
1
Last page
10
Publisher
CEUR-WS.org
Repository
http://hdl.handle.net/2117/113535 Open in new window
URL
http://ceur-ws.org/Vol-1810/DOLAP_paper_28.pdf Open in new window
Abstract
MapReduce is a popular programming model for distributed processing of large data sets. Apache Hadoop is one of the most common open-source implementations of such paradigm. Performance analysis of concurrent job executions has been recognized as a challenging problem, at the same time, that it may provide reasonably accurate job response time at significantly lower cost than experimental evaluation of real setups. In this paper, we tackle the challenge of defining MapReduce performance models f...
Citation
Glushkova, D., Jovanovic, P., Abelló, A. MapReduce performance models for Hadoop 2.x. A: International Workshop On Design, Optimization, Languages and Analytical Processing of Big Data. "Proceedings of the Workshops of the EDBT/ICDT 2017 Joint Conference (EDBT/ICDT 2017): Venice, Italy, March 21-24, 2017". Venice: CEUR-WS.org, 2017, p. 1-10.
Keywords
Hadoop 2.x, MapReduce performance models, Mean value analysis, Queuing theory
Group of research
DTIM - Database Technologies and lnformation Management Group
inLab FIB
inSSIDE - integrated Software, Service, Information and Data Engineering

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