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ALOJA: A Benchmarking and Predictive Platform for Big Data Performance Analysis

  • Nicolas Poggi
  • Josep Ll. Berral
  • David Carrera
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10044)

Abstract

The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of cost-effectiveness of Big Data deployments. The development of the project over its first year, has resulted in a open source benchmarking platform, an online public repository of results with over 42,000 Hadoop job runs, and web-based analytic tools to gather insights about system’s cost-performance (ALOJA’s Web application, tools, and sources available at http://aloja.bsc.es). This article describes the evolution of the project’s focus and research lines from over a year of continuously benchmarking Hadoop under different configuration and deployments options, presents results, and discusses the motivation both technical and market-based of such changes. During this time, ALOJA’s target has evolved from a previous low-level profiling of Hadoop runtime, passing through extensive benchmarking and evaluation of a large body of results via aggregation, to currently leveraging Predictive Analytics (PA) techniques. Modeling benchmark executions allow us to estimate the results of new or untested configurations or hardware set-ups automatically, by learning techniques from past observations saving in benchmarking time and costs.

Notes

Acknowledgements

This work is partially supported the BSC-Microsoft Research Centre, the Spanish Ministry of Education (TIN2012-34557), the MINECO Severo Ochoa Research program (SEV-2011-0067) and the Generalitat de Catalunya (2014-SGR-1051).

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Nicolas Poggi
    • 1
  • Josep Ll. Berral
    • 1
  • David Carrera
    • 1
  1. 1.Barcelona Supercomputing Center (BSC)Universitat Politcnica de Catalunya (UPC-BarcelonaTech)BarcelonaSpain

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