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This article presents ALOJA-Machine Learning (ALOJA-ML) an extension to theALOJA project that uses machine learning techniques to interpret Hadoopbenchmark performance data and performance tuning; here we detail the approach,efficacy of the model and initial results. Hadoop presents a complex executionenvironment, where costs and performance depends on a large number of software(SW) configurations and on multiple hardware (HW) deployment choices. Theseresults are accompanied by a test bed and tools to deploy and evaluate thecost-effectiveness of the different hardware configurations, parameter tunings,and Cloud services. Despite early success within ALOJA from expert-guidedbenchmarking, it became clear that a genuinely comprehensive study requiresautomation of modeling procedures to allow a systematic analysis of large andresource-constrained search spaces. ALOJA-ML provides such an automated systemallowing knowledge discovery by modeling Hadoop executions from observedbenchmarks across a broad set of configuration parameters. The resultingperformance models can be used to forecast execution behavior of variousworkloads; they allow 'a-priori' prediction of the execution times for newconfigurations and HW choices and they offer a route to model-based anomalydetection. In addition, these models can guide the benchmarking explorationefficiently, by automatically prioritizing candidate future benchmark tests.Insights from ALOJA-ML's models can be used to reduce the operational time onclusters, speed-up the data acquisition and knowledge discovery process, andimportantly, reduce running costs. In addition to learning from the methodologypresented in this work, the community can benefit in general from ALOJAdata-sets, framework, and derived insights to improve the design and deploymentof Big Data applications.

ALOJA-ML: A Framework for Automating Characterization and Knowledge Discovery in Hadoop Deployments