# A Decision Tree Based Approach Towards Adaptive Profiling of Distributed Applications

## TL;DR

This paper introduces an automated, decision tree-based profiling method for distributed applications that efficiently models complex performance spaces without prior assumptions, improving accuracy and handling irregularities.

## Contribution

It presents a novel approach using oblique decision trees for adaptive profiling of distributed applications' configuration spaces, outperforming existing methods.

## Key findings

- Outperforms state-of-the-art profiling techniques.
- Effectively captures performance irregularities and discontinuities.
- Allows user-guided sampling for better accuracy and coverage.

## 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.