Configuration Optimization Tool
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The document describes a configuration optimization tool that aims to automatically optimize the configuration of big data technologies. It does this by running experiments on data intensive applications, measuring performance under different configurations, and using this data to recommend optimal configurations. The tool implements two approaches for optimization - Bayesian optimization and transfer learning. It consists of several components, including an experimental suite to run tests, an optimization module, interfaces to various big data technologies, and a performance repository to store results. The goal is to help users like SMEs reduce the time and cost of testing and configuring big data applications between releases.
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