Towards Realistic, Applicable and Feasible Configuration-Aware Performance Modeling
Bibliographic record
Abstract
In modern software systems, configurability has become essential for optimizing performance across varying scenarios and user demands. However, predicting performance in configurable software remains challenging due to the complex interplay between configuration settings and workload characteristics. Existing performance models lack applicability and ignore the combined influence of configurations and workloads, limiting their applicability in dynamic environments. Additionally, current methods focus on either configurations or workloads in isolation, leaving the interactions between the two insufficiently explored. We conclude these challenges into three parts, which are realistic, applicable and feasible for the performance modeling, This thesis addresses the challenges through four projects. We conduct an empirical study to understand the complex relationships between configurations, workloads, and performance outcomes. Additionally, we develop a systematic sampling method to enhance the applicability and accuracy of configuration performance models, allowing models to learn from historical data actively. To further improve performance prediction, we propose a hybrid modeling approach that integrates configuration and workload variations, thereby increasing model simplicity and precision. Finally, we explore applying large language models (LLMs) to streamline the modeling process, embedding LLM insights into traditional methods to reduce the cost and complexity of performance modeling. These contributions aim to create robust performance models that better support software configuration and workload management, enhancing system reliability and efficiency.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".