ALOJA: A benchmarking and predictive platform for big data performance analysis

Abstract

The main goals of the ALOJA research project from BSC-MSR, are to explore and automate the characterization of costeffectiveness 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 costperformance (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.