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

Autor
Berral, J.; Poggi, N.; Carrera, D.; Call, A.; Reinauer, R.; Green, D.
Tipus d'activitat
Article en revista
Revista
IEEE Transactions on emerging topics in computing
Data de publicació
2017-10
Volum
5
Número
4
Pàgina inicial
480
Pàgina final
493
DOI
https://doi.org/10.1109/TETC.2015.2496504 Obrir en finestra nova
Projecte finançador
Computación de Altas Prestaciones VI
European Research Council (Grant agreement No 639595) - Hi-EST
Repositori
http://hdl.handle.net/2117/104910 Obrir en finestra nova
URL
http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7312954 Obrir en finestra nova
Resum
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...
Citació
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.
Paraules clau
Benchmarks, Data-center management, Execution experiences, Hadoop, Machine learning, Modeling and prediction
Grup de recerca
CAP - Grup de Computació d'Altes Prestacions

Participants

  • Berral Garcia, Josep Lluis  (autor)
  • Poggi Mastrokalo, Nicolas  (autor)
  • Carrera Perez, David  (autor)
  • Call, Aaron  (autor)
  • Reinauer, Rob  (autor)
  • Green, Daron  (autor)

Arxius