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Issue No. 04 - Oct.-Dec. (2017 vol. 5)
ISSN: 2168-6750
pp: 480-493
Josep Lluis Berral , Barcelona Supercomputing Center, Universitat Politècnica de Catalunya, Barcelona, Spain
Nicolas Poggi , Barcelona Supercomputing Center, Universitat Politècnica de Catalunya, Barcelona, Spain
David Carrera , Barcelona Supercomputing Center, Universitat Politècnica de Catalunya, Barcelona, Spain
Aaron Call , Barcelona Supercomputing Center, Universitat Politècnica de Catalunya, Barcelona, Spain
Rob Reinauer , Microsoft Corporation, Redmond, WA, USA
Daron Green , Microsoft Corporation, Redmond, WA, USA
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 Barcelona Supercomputing Center 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 open, vendor-neutral repository, featuring over 40 000 Hadoop job executions and their performance details. The repository is accompanied by a test bed and tools to deploy and evaluate the cost-effectiveness of different hardware configurations, parameters, and cloud services. Despite early success within ALOJA, a comprehensive study requires automation of modeling procedures to allow an analysis of large and resource-constrained search spaces. The predictive analytics extension, ALOJA-ML, provides an automated system allowing knowledge discovery by modeling environments from observed executions. The resulting models can forecast execution behaviors, predicting execution times for new configurations and hardware choices. That also enables model-based anomaly detection or efficient benchmark guidance by prioritizing executions. In addition, the community can benefit from ALOJA data sets and framework to improve the design and deployment of big data applications.
INDEX TERMS
Benchmark testing, Hardware, Predictive models, Software, Analytical models, Licenses, Big data
CITATION

J. L. Berral, N. Poggi, D. Carrera, A. Call, R. Reinauer and D. Green, "ALOJA: A Framework for Benchmarking and Predictive Analytics in Hadoop Deployments," in IEEE Transactions on Emerging Topics in Computing, vol. 5, no. 4, pp. 480-493, 2017.
doi:10.1109/TETC.2015.2496504
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