Title: A Decision Tree Based Approach Towards Adaptive Profiling of Distributed Applications,
Abstract: The adoption of the distributed paradigm has allowed applications to increase
their scalability, robustness and fault tolerance, but it has also complicated
their 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 makes
no assumptions on application structure. Our approach utilizes oblique Decision
Trees in order to recursively partition an application's configuration space in
disjoint regions, choose a set of representative samples from each subregion
according to a defined policy and return a model for the entire space as a
composition of linear models over each subregion. An extensive evaluation over
real-life applications and synthetic performance functions showcases that our
scheme outperforms other state-of-the-art profiling methodologies. It
particularly excels at reflecting abnormalities and discontinuities of the
performance function, allowing the user to influence the sampling policy based
on the modeling accuracy and the space coverage.