Abstract
To ensure the quality of frequent releases in DevOps context, performance models enable system performance simulation and prediction. However, building performance models for microservice or serverless-based applications in DevOps is costly and error-prone. Thus, we propose to employ model discovery learning for performance models automatically. To generate basic models to represent the application, we first introduce performance-related TOSCA models as architectural models. Then we transform TOSCA models into layered queueing network models. A main challenge of performance model generation is model parametrization. We propose to learn parametric dependencies from monitoring data and systems analysis to capture the relationship between input data and resource demand. With frequent releases of new features, we consider employing detecting parametric dependencies incrementally to keep updating performance models in each iteration.
Supervised by: G. Casale and A. Filieri.
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Wang, R. (2021). Learning Performance Models Automatically. In: Hacid, H., et al. Service-Oriented Computing – ICSOC 2020 Workshops. ICSOC 2020. Lecture Notes in Computer Science(), vol 12632. Springer, Cham. https://doi.org/10.1007/978-3-030-76352-7_6
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DOI: https://doi.org/10.1007/978-3-030-76352-7_6
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