Abstract
Nowadays, a lot of data is produced every second and it needs to be processed immediately. Processing such unbounded streams of data is often applied in a distributed environment in order to achieve high throughput. There is a challenge to predict the performance-related characteristics of such applications. Knowledge of these properties is essential for decisions about the amount of needed computational resources, how the computations should be spread in the distributed environment, etc.
In this paper, we propose a model to represent such streaming applications with the respect to their performance related properties. We present a conversion of the model to Colored Petri Nets (CPNs) which is used for performance analysis of the original application. The behavior of the proposed model and its conversion to the CPNs is validated through experiments. Our prediction was able to achieve nearly 100 % precise maximum delays of real stream processing applications.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Apache Storm. https://storm.apache.org/
Benoit, A., Çatalyürek, Ü.V., Robert, Y., Saule, E.: A survey of pipelined workflow scheduling: models and algorithms. ACM Comput. Surv. (CSUR) 45(4), 50 (2013)
Bouillard, A., Phan, L.T., Chakraborty, S.: Lightweight modeling of complex state dependencies in stream processing systems. In: 15th IEEE Real-Time and Embedded Technology and Applications Symposium, 2009, RTAS 2009, pp. 195–204. IEEE (2009)
Chakraborty, S., Phan, L.T., Thiagarajan, P.: Event count automata: a state-based model for stream processing systems. In: 26th IEEE International Real-Time Systems Symposium, 2005, RTSS 2005, pp. 87–98. IEEE (2005)
Chakraborty, S., Thiele, L.: A new task model for streaming applications and its schedulability analysis. In: Proceedings of Design, Automation and Test in Europe, 2005, pp. 486–491. IEEE (2005)
Evans, M., Hastings, N., Peacock, B.: Probability density function and probability function. In: Hastings, N. (ed.) Statistical Distributions, pp. 9–11. Wiley, New York (2000)
Gottumukkala, R.N., Shepherd, M.D., Sun, T.: Validation and analysis of JDF workflows using colored petri nets. US Patent 7,734,492, 8 June 2010
Jensen, K., Kristensen, L.M.: Coloured Petri Nets: Modelling and Validation of Concurrent Systems. Springer, Heidelberg (2009)
Rygielski, P., Kounev, S.: Data center network throughput analysis using queueing petri nets. In: 2014 IEEE 34th International Conference on Distributed Computing Systems Workshops (ICDCSW), pp. 100–105. IEEE (2014)
Wieczorek, M., Hoheisel, A., Prodan, R.: Towards a general model of the multi-criteria workflow scheduling on the grid. Future Gener. Comput. Syst. 25(3), 237–256 (2009)
Acknowledgements
This work was supported by the Czech national research project GBP103/12/G084. The hardware infrastructure was provided by the METACentrum under the programme LM 2010005.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Nalepa, F., Batko, M., Zezula, P. (2015). Model for Performance Analysis of Distributed Stream Processing Applications. In: Chen, Q., Hameurlain, A., Toumani, F., Wagner, R., Decker, H. (eds) Database and Expert Systems Applications. Globe DEXA 2015 2015. Lecture Notes in Computer Science(), vol 9262. Springer, Cham. https://doi.org/10.1007/978-3-319-22852-5_42
Download citation
DOI: https://doi.org/10.1007/978-3-319-22852-5_42
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-22851-8
Online ISBN: 978-3-319-22852-5
eBook Packages: Computer ScienceComputer Science (R0)