Automated Performance Tuning for Highly-Configurable Software Systems

TL;DR

This paper introduces ConfRL, an automated reinforcement learning-based approach to optimize performance in highly-configurable software systems by efficiently exploring large configuration spaces.

Contribution

It presents ConfRL, a novel reinforcement learning method that uses sampling, clustering, and state reduction to automate performance tuning in complex software systems.

Findings

ConfRL improves performance in real-world server programs.

It reduces tuning time compared to manual methods.

ConfRL effectively manages large configuration spaces.

Abstract

Performance is an important non-functional aspect of the software requirement. Modern software systems are highly-configurable and misconfigurations may easily cause performance issues. A software system that suffers performance issues may exhibit low program throughput and long response time. However, the sheer size of the configuration space makes it challenging for administrators to manually select and adjust the configuration options to achieve better performance. In this paper, we propose ConfRL, an approach to tune software performance automatically. The key idea of ConfRL is to use reinforcement learning to explore the configuration space by a trial-and-error approach and to use the feedback received from the environment to tune configuration option values to achieve better performance. To reduce the cost of reinforcement learning, ConfRL employs sampling, clustering, and dynamic state reduction techniques to keep states in a large configuration space manageable. Our evaluation of four real-world highly-configurable server programs shows that ConfRL can efficiently and effectively guide software systems to achieve higher long-term performance.