White-Box Performance-Influence Models: A Profiling and Learning Approach

TL;DR

This paper introduces a white-box modeling approach that profiles and learns method-level performance influences in configurable software systems, improving understanding and debugging over traditional black-box models.

Contribution

It presents a novel two-step profiling and learning method for method-level performance influence modeling in configurable systems, enhancing accuracy and interpretability.

Findings

Effectively identifies configuration-sensitive methods

Learns accurate performance-influence models

Demonstrates efficiency on real-world Java systems

Abstract

Many modern software systems are highly configurable, allowing the user to tune them for performance and more. Current performance modeling approaches aim at finding performance-optimal configurations by building performance models in a black-box manner. While these models provide accurate estimates, they cannot pinpoint causes of observed performance behavior to specific code regions. This does not only hinder system understanding, but it also complicates tracing the influence of configuration options to individual methods. We propose a white-box approach that models configuration-dependent performance behavior at the method level. This allows us to predict the influence of configuration decisions on individual methods, supporting system understanding and performance debugging. The approach consists of two steps: First, we use a coarse-grained profiler and learn performance-influence models for all methods, potentially identifying some methods that are highly configuration- and performance-sensitive, causing inaccurate predictions. Second, we re-measure these methods with a fine-grained profiler and learn more accurate models, at higher cost, though. By means of 9 real-world Java software systems, we demonstrate that our approach can efficiently identify configuration-relevant methods and learn accurate performance-influence models.