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Tulsa: A Tool for Transforming UML to Layered Queueing Networks for Performance Analysis of Data Intensive Applications

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Quantitative Evaluation of Systems (QEST 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10503))

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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.

This work is partially supported by the European Commission grant no. 644869, DICE.

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Notes

  1. 1.

    http://line-solver.sourceforge.net/.

  2. 2.

    https://github.com/layeredqueuing/V5/tree/master/lqns.

  3. 3.

    http://www.eclipse.org/epsilon/.

References

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Authors and Affiliations

  1. Imperial College London, London, SW7 2AZ, UK

    Chen Li & Giuliano Casale

  2. Carleton University, Ottawa, K1S 5B6, Canada

    Taghreed Altamimi, Mana Hassanzadeh Zargari & Dorina Petriu

Authors
  1. Chen Li
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  2. Taghreed Altamimi
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  3. Mana Hassanzadeh Zargari
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  4. Giuliano Casale
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  5. Dorina Petriu
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Corresponding author

Correspondence to Chen Li .

Editor information

Editors and Affiliations

  1. Inria, Rennes, France

    Nathalie Bertrand

  2. University of Trieste, Trieste, Italy

    Luca Bortolussi

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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: Bertrand, N., Bortolussi, L. (eds) Quantitative Evaluation of Systems. QEST 2017. Lecture Notes in Computer Science(), vol 10503. Springer, Cham. https://doi.org/10.1007/978-3-319-66335-7_18

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  • DOI: https://doi.org/10.1007/978-3-319-66335-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-66334-0

  • Online ISBN: 978-3-319-66335-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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