Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis (1709.02280v1)

Abstract: Modern software systems provide many configuration options which significantly influence their non-functional properties. To understand and predict the effect of configuration options, several sampling and learning strategies have been proposed, albeit often with significant cost to cover the highly dimensional configuration space. Recently, transfer learning has been applied to reduce the effort of constructing performance models by transferring knowledge about performance behavior across environments. While this line of research is promising to learn more accurate models at a lower cost, it is unclear why and when transfer learning works for performance modeling. To shed light on when it is beneficial to apply transfer learning, we conducted an empirical study on four popular software systems, varying software configurations and environmental conditions, such as hardware, workload, and software versions, to identify the key knowledge pieces that can be exploited for transfer learning. Our results show that in small environmental changes (e.g., homogeneous workload change), by applying a linear transformation to the performance model, we can understand the performance behavior of the target environment, while for severe environmental changes (e.g., drastic workload change) we can transfer only knowledge that makes sampling more efficient, e.g., by reducing the dimensionality of the configuration space.

• The paper demonstrates that simple linear transformations can effectively transfer performance models across similar environments, reducing sampling effort.
• The study shows that key configuration options consistently influence performance across settings, enabling focused transfer learning approaches.
• The research finds that knowledge of invalid configurations is transferable, guiding the avoidance of error-prone sampling regions.

Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis

Performance modeling for configurable software systems poses a significant challenge due to the extensive configuration space and various interactions between configuration options. The paper "Transfer Learning for Performance Modeling of Configurable Systems: An Exploratory Analysis" addresses the potential of applying transfer learning to alleviate the cost of performance model construction across different environments.

The authors conduct an empirical paper utilizing four highly configurable software systems, analyzing how performance models can be transferred when environmental conditions such as hardware, workload, and software versions change. The paper explores the properties of transfer learning that can be leveraged to improve model accuracy and reduce the sampling effort traditionally required for model construction.

Core Insights and Contributions

1. Performance Behavior Consistency: The paper investigates whether performance models exhibit consistency in behavior across varied environments. A substantial finding is that linear transformations frequently suffice to transfer models in cases of minor environmental changes, such as homogeneous workload shifts or small hardware upgrades. This finding has significant implications as it suggests that in many practical scenarios, performance models need not be reconstructed from scratch, but instead can be translated using straightforward mathematical formulations.
2. Option and Interaction Influence: The paper further examines whether influential configuration options and interactions retain their relative impact across environments. The results suggest that although not all configuration options significantly affect performance, the options that do tend to maintain their influence consistently across different settings. This stability supports the notion that transfer learning can focus on key configuration dimensions to reduce effort while maintaining model fidelity.
3. Invalid Configurations: A recurring challenge in performance modeling arises from invalid configurations, which cause failures or timeouts. The findings reveal that knowledge regarding invalid configurations is largely transferable across similar environments. This insight can be practically applied by avoiding regions known to induce invalid configurations, thus concentrating efforts on successful sampling.
4. Transfer Learning Potential: The detailed analysis conducted in the paper provides compelling evidence for the feasibility of deploying transfer learning in real-world performance modeling. By delineating the circumstances under which performance models can be transferred, the authors contribute a framework that practitioners can leverage when navigating changes in software deployment conditions.

Implications for Future Research

The authors suggest that the success of transfer learning in performance modeling could introduce efficiencies in various scenarios, such as reducing the need for extensive benchmarking when moving applications from testing to production environments or when deploying performance tuning in adaptive systems. The paper sets a foundation for exploring more sophisticated transfer learning methodologies, like non-linear transformations and advanced machine learning paradigms, to further enhance performance prediction across diverse and rapidly changing computing environments.

In conclusion, this exploratory analysis solidifies the potential of transfer learning to significantly reduce the costs associated with performance modeling of configurable systems. It expands the understanding of when and why transfer learning can be effectively applied, offering a roadmap for future innovations in this challenging yet critical domain.