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The adoption of the distributed paradigm has allowed applications to increasetheir scalability, robustness and fault tolerance, but it has also complicatedtheir structure, leading to an exponential growth of the applications'configuration space and increased difficulty in predicting their performance.In this work, we describe a novel, automated profiling methodology that makesno assumptions on application structure. Our approach utilizes oblique DecisionTrees in order to recursively partition an application's configuration space indisjoint regions, choose a set of representative samples from each subregionaccording to a defined policy and return a model for the entire space as acomposition of linear models over each subregion. An extensive evaluation overreal-life applications and synthetic performance functions showcases that ourscheme outperforms other state-of-the-art profiling methodologies. Itparticularly excels at reflecting abnormalities and discontinuities of theperformance function, allowing the user to influence the sampling policy basedon the modeling accuracy and the space coverage.

A Decision Tree Based Approach Towards Adaptive Profiling of Distributed Applications