Towards Realistic, Applicable and Feasible Configuration-Aware Performance Modeling

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...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Proceedings (IEEE/ACM International Conference on Software Engineering Companion. Online) s. 71 - 75
Hlavní autor: Xia, Yuanjie
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 27.04.2025
Témata:
Accuracy
Complexity theory
configurable software
Costs
Data models
Large language models
performance modeling
Predictive models
Software engineering
Software performance
Software systems
Systematics
ISSN:2574-1934
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí: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.
ISSN:2574-1934
DOI:10.1109/ICSE-Companion66252.2025.00027