Abstract
Data streaming applications are an important class of data-intensive systems and performance is an essential quality of such systems. Current component-based performance prediction approaches are not sufficient for modeling and predicting the performance of those systems, because the models require elaborate manual engineering to approximate the behavior of data streaming applications that include stateful asynchronous operations, such as windowing operations, and because the simulations for these models do not support the metrics that are specific to data streaming applications. In this paper, we present a modeling language, a simulation and a case-study-based evaluation of the prediction accuracy of an approach for modeling systems that contain stateful asynchronous operations. Our approach directly represents these operations and simulates their behavior. We compare measurements of relevant performance metrics to performance simulation results for a system that processes smart meter readings. To assess the prediction accuracy of our model, we vary both the configuration of the streaming application, such as window sizes, as well as the characteristics of the input data, i.e., the number of smart meters. Our evaluation shows that our model yields prediction results that are competitive with a state-of-the-art baseline model without incurring the additional manual engineering overhead.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Akidau, T., et al.: The dataflow model: a practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing. Proc. VLDB Endow. 8(12) (2015). https://doi.org/10.14778/2824032.2824076
Aliabadi, S.K., et al.: Analytical composite performance models for big data applications. J. Netw. Comput. Appl. 142, 63–75 (2019)
Basili, V.R., Caldiera, G., Rombach, H.D.: The goal question metric approach. In: Encyclopedia of Software Engineering - 2 Volume Set. Wiley (1994)
Becker, M., et al.: Performance analysis of self-adaptive systems for requirements validation at design-time. In: ACM SIGSOFT QoSA 2013. ACM (2013). https://doi.org/10.1145/2465478.2465489
Casale, G., Li, C.: Enhancing big data application design with the DICE framework. In: Advances in Service-Oriented and Cloud Computing - Workshops of ESOCC (2017)
Castiglione, A., et al.: Modeling performances of concurrent big data applications. Softw. Pract. Exper. 45(8), 1127–1144 (2015)
DICE consortium: Deliverable 3.4 DICE simulation tools (2017). http://www.dice-h2020.eu/deliverables/. European Union’s Horizon 2020 Programme
Hummel, O., et al.: A collection of software engineering challenges for big data system development. In: Euromicro SEAA. IEEE (2018)
Jerzak, Z., Ziekow, H.: DEBS 2014 Grand Challenge: Smart homes - DEBS.org. https://debs.org/grand-challenges/2014/
Jerzak, Z., Ziekow, H.: The DEBS 2014 grand challenge. In: DEBS 2014. ACM, New York (2014). https://doi.org/10.1145/2611286.2611333
Kroß, J., Krcmar, H.: Model-based performance evaluation of batch and stream applications for big data. In: MASCOTS. IEEE (2017)
Kroß, J., Krcmar, H.: Pertract: model extraction and specification of big data systems for performance prediction by the example of apache spark and hadoop. Big Data Cogn. Comput. 3(3), 47 (2019)
Maddodi, G., Jansen, S., Overeem, M.: Aggregate architecture simulation in event-sourcing applications using layered queuing networks. In: ICPE 2020. ACM (2020)
Reussner, R.H., et al.: Modeling and Simulating Software Architectures - The Palladio Approach. MIT Press, Cambridge (2016)
Runeson, P., Höst, M.: Guidelines for conducting and reporting case study research in software engineering. Empir. Softw. Eng. 14(2), 131–164 (2009)
Sachs, K.: Performance modeling and benchmarking of event-based systems. Ph.D. thesis, Darmstadt University of Technology (2011)
Singh, S., et al.: Towards extraction of message-based communication in mixed-technology architectures for performance model. In: ICPE 2021. ACM (2021). https://doi.org/10.1145/3447545.3451201
Werle, D., Seifermann, S., Koziolek, A.: Data stream operations as first-class entities in palladio. In: SSP 2019. Softwaretechnik Trends (2019)
Werle, D., Seifermann, S., Koziolek, A.: Data stream operations as first-class entities in component-based performance models. In: Jansen, A., Malavolta, I., Muccini, H., Ozkaya, I., Zimmermann, O. (eds.) ECSA 2020. LNCS, vol. 12292, pp. 148–164. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58923-3_10
Werle, D., Seifermann, S., Koziolek, A.: Data Set of Publication on Accurate Performance Predictions with Component-based Models of Data Streaming Applications (2022). https://doi.org/10.5281/zenodo.6762128
Wu, E., Diao, Y., Rizvi, S.: High-performance complex event processing over streams. In: SIGMOD. ACM (2006)
Acknowledgements
This work was supported by KASTEL Security Research Labs and by the German Research Foundation (DFG) under project number 432576552, HE8596/1-1 (FluidTrust).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Werle, D., Seifermann, S., Koziolek, A. (2022). Accurate Performance Predictions with Component-Based Models of Data Streaming Applications. In: Gerostathopoulos, I., Lewis, G., Batista, T., Bureš, T. (eds) Software Architecture. ECSA 2022. Lecture Notes in Computer Science, vol 13444. Springer, Cham. https://doi.org/10.1007/978-3-031-16697-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-031-16697-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-16696-9
Online ISBN: 978-3-031-16697-6
eBook Packages: Computer ScienceComputer Science (R0)