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ALOJA: A framework for benchmarking and predictive analytics in Hadoop deployments

Author
Berral, J.; Poggi, N.; Carrera, D.; Call, A.; Reinauer, R.; Green, D.
Type of activity
Journal article
Journal
IEEE Transactions on emerging topics in computing
Date of publication
2017-10
Volume
5
Number
4
First page
480
Last page
493
DOI
https://doi.org/10.1109/TETC.2015.2496504 Open in new window
Project funding
Computación de Altas Prestaciones VI
European Research Council (Grant agreement No 639595) - Hi-EST
Repository
http://hdl.handle.net/2117/104910 Open in new window
URL
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7312954 Open in new window
Abstract
This article presents the ALOJA project and its analytics tools, which leverages machine learning to interpret Big Data benchmark performance data and tuning. ALOJA is part of a long-term collaboration between BSC and Microsoft to automate the characterization of cost-effectiveness on Big Data deployments, currently focusing on Hadoop. Hadoop presents a complex run-time environment, where costs and performance depend on a large number of configuration choices. The ALOJA project has created an op...
Citation
Berral, J., Poggi, N., Carrera, D., Call, A., Reinauer, R., Green, D. ALOJA: A framework for benchmarking and predictive analytics in Hadoop deployments. "IEEE Transactions on emerging topics in computing", Oct-Des 2017, vol. 5, núm. 4, p. 480-493.
Keywords
Benchmarks, Data-center management, Execution experiences, Hadoop, Machine learning, Modeling and prediction
Group of research
CAP - High Performace Computing Group

Participants

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