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H-word: Supporting job scheduling in Hadoop with workload-driven data redistribution

Author
Jovanovic, P.; Romero, O.; Calders, T.; Abello, A.
Type of activity
Presentation of work at congresses
Name of edition
20th East European Conference on Advances in Databases and Information Systems
Date of publication
2016
Presentation's date
2016-08
Book of congress proceedings
Advances in Databases and Information Systems - 20th East European Conference, ADBIS 2016, Proceedings
First page
306
Last page
320
DOI
https://doi.org/10.1007/978-3-319-44039-2_21 Open in new window
Repository
http://hdl.handle.net/2117/103769 Open in new window
URL
http://link.springer.com/chapter/10.1007/978-3-319-44039-2_21 Open in new window
Abstract
The final publication is available at http://link.springer.com/chapter/10.1007/978-3-319-44039-2_21 Today’s distributed data processing systems typically follow a query shipping approach and exploit data locality for reducing network traffic. In such systems the distribution of data over the cluster resources plays a significant role, and when skewed, it can harm the performance of executing applications. In this paper, we addressthe challenges of automatically adapting the distribution of dat...
Citation
Jovanovic, P., Romero, O., Calders, T., Abello, A. H-word: Supporting job scheduling in Hadoop with workload-driven data redistribution. A: Conference on Advances in Databases and Information Systems. "Advances in Databases and Information Systems - 20th East European Conference, ADBIS 2016, Proceedings". Praga: 2016, p. 306-320.
Keywords
Computer programming, Data handling, Data intensive, Data locality, Data redistribution, Distributed data processing, Execution scenario, Generic algorithm, Information systems, Input applications, Performance Gain, Scheduling
Group of research
DTIM - Database Technologies and lnformation Management Group
IMP - Information Modelling and Processing
inLab FIB
inSSIDE - integrated Software, Service, Information and Data Engineering

Participants

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