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TeaStore: A Micro-Service Reference Application for Research Use

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Systems Benchmarking

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This chapter describes TeaStore12 (Kistowski et al, 2018, TeaStore: A Micro-Service Reference Application for Benchmarking, Modeling and Resource Management Research. In: Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2018). (Milwaukee, WI, USA). IEEE Computer Society: Washington, DC, USA), a micro-service-based test and reference application that can be used as a benchmarking framework by researchers. It also demonstrates the application of TeaStore as a test and benchmarking workload by using it as a reference application showing its use in an energy-efficiency benchmarking context to evaluate the energy efficiency of service placements.

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References

  • Aderaldo, C. M., Mendonça, N. C., Pahl, C., & Jamshidi, P. (2017). Benchmark requirements for microservices architecture research. In Proceedings of the 1st International Workshop on Establishing the Community-Wide Infrastructure for Architecture-Based Software Engineering, Buenos Aires, Argentina (pp. 8–13). Piscataway, NJ: IEEE.

    Google Scholar 

  • Basmadjian, R., Ali, N., Niedermeier, F., Meer, H. de, & Giuliani, G. (2011). A methodology to predict the power consumption of servers in data centres. In Proceedings of the 2nd International Conference on Energy-Efficient Computing and Networking (e-Energy’11), New York, NY, USA (pp. 1–10). New York, NY: ACM.

    Google Scholar 

  • Becker, S., Koziolek, H., & Reussner, R. (2009). The Palladio component model for model-driven performance prediction. Journal of Systems and Software, 82(1), 3–22. Amsterdam: Elsevier Science.

    Google Scholar 

  • Beloglazov, A., Abawajy, J., & Buyya, R. (2012). Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing. Future Generation Computer Systems, 28(5), 755–768. Amsterdam: Elsevier Science.

    Google Scholar 

  • Brunnert, A., Hoorn, A. van, Willnecker, F., Danciu, A., Hasselbring, W., Heger C., et al. (2015). Performance-oriented DevOps: A research agenda. Tech. rep. SPEC-RG-2015-01. Gainesville, VA, USA: SPEC RG—DevOps Performance Working Group, Standard Performance Evaluation Corporation (SPEC).

    Google Scholar 

  • Happe, J., Koziolek, H., & Reussner, R. (2011). Facilitating performance predictions using software components. IEEE Software, 28(3), 27–33. Piscataway, NJ: IEEE.

    Google Scholar 

  • Hoorn, A. van, Rohr, M., Hasselbring, W., Waller, J., Ehlers, J., Frey, S., et al. (2009). Continuous monitoring of software services: Design and application of the Kieker framework. Tech. rep. TR-0921. Department of Computer Science, Kiel University, Germany.

    Google Scholar 

  • Hoorn, A. van, Waller, J., & Hasselbring, W. (2012). Kieker: A framework for application performance monitoring and dynamic software analysis. In Proceedings of the 3rd ACM/SPEC International Conference on Performance Engineering (ICPE 2012), Boston, MA, USA (pp. 247–248). New York, NY: ACM.

    Chapter  Google Scholar 

  • Ilyushkin, A., Ali-Eldin, A., Herbst, N. R., Papadopoulos, A. V., Ghit, B., Epema, D., et al. (2017). An experimental performance evaluation of autoscaling policies for complex workflows. In Proceedings of the 8th ACM/SPEC International Conference on Performance Engineering (ICPE 2017), L’Aquila, Italy (pp. 75–86). New York, NY: ACM.

    Chapter  Google Scholar 

  • Kistowski, J. von, Deffner, M., & Kounev, S. (2018). Run-time prediction of power consumption for component deployments. In Proceedings of the 15th IEEE International Conference on Autonomic Computing (ICAC 2018), Trento, Italy. Piscataway, NJ: IEEE.

    Google Scholar 

  • Kistowski, J. von, Eismann, S., Schmitt, N., Bauer, A., Grohmann, J., & Kounev, S. (2018). TeaStore: A micro-service reference application for benchmarking, modeling and resource management research. In Proceedings of the 26th IEEE International Symposium on the Modelling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2018), Milwaukee, WI, USA. Washington, DC: IEEE Computer Society.

    Google Scholar 

  • Lemire, D., & Maclachlan, A. (2005). Slope one predictors for online rating-based collaborative filtering. In Proceedings of the 2005 SIAM International Conference on Data Mining (SDM 2005), Newport Beach, CA, USA (pp. 471–475). Philadelphia, PA: Society for Industrial and Applied Mathematics (SIAM).

    Google Scholar 

  • SPECpower Committee (2014). Power and Performance Benchmark Methodology V2.2. Gainesville, VA: Standard Performance Evaluation Corporation (SPEC).

    Google Scholar 

  • Willnecker, F., Dlugi, M., Brunnert, A., Spinner, S., Kounev, S., & Krcmar, H. (2015). Comparing the accuracy of resource demand measurement and estimation techniques. In M. Beltrán, W. Knottenbelt & J. Bradley (Eds.), Computer Performance Engineering— Proceedings of the 12th European Performance Engineering Workshop (EPEW 2015), Madrid, Spain. Lecture Notes in Computer Science (Vol. 9272, pp. 115–129). Berlin/Heidelberg: Springer.

    Google Scholar 

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Kounev, S., Lange, KD., Kistowski, J.v. (2020). TeaStore: A Micro-Service Reference Application for Research Use. In: Systems Benchmarking. Springer, Cham. https://doi.org/10.1007/978-3-030-41705-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-41705-5_14

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-41704-8

  • Online ISBN: 978-3-030-41705-5

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