Abstract
Motivated by the problem of detecting software performance anti-patterns in data-intensive applications (DIAs), we present a tool, Tulsa, for transforming software architecture models specified through UML into Layered Queueing Networks (LQNs), which are analytical performance models used to capture contention across multiple software layers. In particular, we generalize an existing transformation based on the Epsilon framework to generate LQNs from UML models annotated with the DICE profile, which extends UML to modelling DIAs based on technologies such as Apache Storm.
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CITATION STYLE
Li, C., Altamimi, T., Zargari, M. H., Casale, G., & Petriu, D. (2017). Tulsa: a tool for transforming UML to layered queueing networks for performance analysis of data intensive applications. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10503 LNCS, pp. 295–299). Springer Verlag. https://doi.org/10.1007/978-3-319-66335-7_18
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