Tulsa: a tool for transforming UML to layered queueing networks for performance analysis of data intensive applications
File(s)
Submitted version
Author(s)
Li, C
Altamimi, T
Zargari, MH
Casale, G
Petriu, D
Type
Conference Paper
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.
Date Issued
2017-08-11
Date Acceptance
2017-08-01
Citation
Lecture Notes in Computer Science, 2017, 10503, pp.295-299
ISBN
9783319663340
ISSN
0302-9743
Publisher
Springer Verlag
Start Page
295
End Page
299
Journal / Book Title
Lecture Notes in Computer Science
Volume
10503
Copyright Statement
© 2017 Springer International Publishing AG. The final publication is available at https://dx.doi.org/10.1007/978-3-319-66335-7_18
Sponsor
Commission of the European Communities
Grant Number
644869
Source
14th International Conference, QEST 2017
Subjects
08 Information And Computing Sciences
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2017-09-05
Finish Date
2017-09-07
Coverage Spatial
Berlin, Germany