  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 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.
