-
Understanding and Auto-Adjusting Performance-Related Configurations
Modern software systems are often equipped with hundreds to thousands of...
read it
-
Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis
Modern software systems provide many configuration options which signifi...
read it
-
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations
Assemblies of modular subsystems are being pressed into service to perfo...
read it
-
Learning Software Configuration Spaces: A Systematic Literature Review
Most modern software systems (operating systems like Linux or Android, W...
read it
-
Lazy Product Discovery in Huge Configuration Spaces
Highly-configurable software systems can have thousands of interdependen...
read it
-
The Case for Automatic Database Administration using Deep Reinforcement Learning
Like any large software system, a full-fledged DBMS offers an overwhelmi...
read it
-
iGen: Dynamic Interaction Inference for Configurable Software
To develop, analyze, and evolve today's highly configurable software sys...
read it
Automated Performance Tuning for Highly-Configurable Software Systems
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.
READ FULL TEXT
Comments
There are no comments yet.