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Minimizing Self-adaptation Overhead in Parallel Stream Processing for Multi-cores

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Book cover Euro-Par 2019: Parallel Processing Workshops (Euro-Par 2019)

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Abstract

Stream processing paradigm is present in several applications that apply computations over continuous data flowing in the form of streams (e.g., video feeds, image, and data analytics). Employing self-adaptivity to stream processing applications can provide higher-level programming abstractions and autonomic resource management. However, there are cases where the performance is suboptimal. In this paper, the goal is to optimize parallelism adaptations in terms of stability and accuracy, which can improve the performance of parallel stream processing applications. Therefore, we present a new optimized self-adaptive strategy that is experimentally evaluated. The proposed solution provided high-level programming abstractions, reduced the adaptation overhead, and achieved a competitive performance with the best static executions.

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Notes

  1. 1.

    https://github.com/dalvangriebler/upl.

References

  1. Aldinucci, M., Danelutto, M., Kilpatrick, P., Torquati, M.: Fastflow: High-Level and Efficient Streaming on Multicore, chap. 13, pp. 261–280. Wiley-Blackwell (2014)

    Google Scholar 

  2. Andrade, H., Gedik, B., Turaga, D.: Fundamentals of Stream Processing: Application Design, Systems, and Analytics. Cambridge University Press, Cambridge (2014)

    Book  Google Scholar 

  3. Gedik, B., Schneider, S., Hirzel, M., Wu, K.L.: Elastic scaling for datastream processing. IEEE Trans. Parallel Distrib. Syst. 25(6), 1447–1463 (2014)

    Article  Google Scholar 

  4. Griebler, D., Danelutto, M., Torquati, M., Fernandes, L.G.: SPar: a DSL for high-level and productive stream parallelism. Parallel Process. Lett. 27(01), 1740005 (2017)

    Article  MathSciNet  Google Scholar 

  5. Griebler, D., Hoffmann, R.B., Danelutto, M., Fernandes, L.G.: Higher-level parallelism abstractions for video applications with SPar. In: Parallel Computing is Everywhere, Proceedings of the International Conference on Parallel Computing, ParCo 2017, pp. 698–707. IOS Press, Bologna, September 2017

    Google Scholar 

  6. Griebler, D., Hoffmann, R.B., Danelutto, M., Fernandes, L.G.: High-level and productive stream parallelism for dedup, ferret, and Bzip2. Int. J. Parallel Prog. 47(2), 253–271 (2018). https://doi.org/10.1007/s10766-018-0558-x

    Article  Google Scholar 

  7. Griebler, D., Vogel, A., De Sensi, D., Danelutto, M., Fernandes, L.G.: Simplifying and implementing service level objectives for stream parallelism. J. Supercomput. (2019). https://doi.org/10.1007/s11227-019-02914-6

  8. Hellerstein, J.L., Diao, Y., Parekh, S., Tilbury, D.M.: Feedback Control of Computing Systems. Wiley, Chichester (2004)

    Book  Google Scholar 

  9. Matteis, T.D., Mencagli, G.: Keep calm and react with foresight: strategies for low-latency and energy-efficient elastic data stream processing. In: Proceedings of the ACM Symposium on Principles and Practice of Parallel Programming, pp. 13:1–13:12 (2016)

    Google Scholar 

  10. Selva, M., Morel, L., Marquet, K., Frenot, S.: A monitoring system for runtime adaptations of streaming applications. In: Proceedings of the Euromicro Conference on Parallel, Distributed and Network-based Processing, pp. 27–34 (2015)

    Google Scholar 

  11. Sensi, D.D., Torquati, M., Danelutto, M.: A reconfiguration algorithm for power-aware parallel applications. ACM Trans. Architect. Code Optim. 13(4), 43:1–43:25 (2016)

    Google Scholar 

  12. Vogel, A., Griebler, D., De Sensi, D., Danelutto, M., Fernandes, L.G.: Autonomic and latency-aware degree of parallelism management in SPar. In: Mencagli, G., et al. (eds.) Euro-Par 2018. LNCS, vol. 11339, pp. 28–39. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-10549-5_3

    Chapter  Google Scholar 

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Acknowledgment

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001, Univ. of Pisa PRA_2018_66 “DECLware: Declarative methodologies for designing and deploying applications”, the FAPERGS 01/2017-ARD project called ParaElastic (No. 17/2551-0000871-5), and the Universal MCTIC/CNPq N\(^\circ \) 28/2018 project called SParCloud (No. 437693/2018-0).

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Authors and Affiliations

  1. School of Technology, Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil

    Adriano Vogel, Dalvan Griebler & Luiz Gustavo Fernandes

  2. Department of Computer Science, University of Pisa, Pisa, Italy

    Marco Danelutto

  3. Laboratory of Advanced Research on Cloud Computing (LARCC), Três de Maio Faculty (SETREM), Três de Maio, Brazil

    Dalvan Griebler

Authors
  1. Adriano Vogel
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  2. Dalvan Griebler
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  3. Marco Danelutto
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  4. Luiz Gustavo Fernandes
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Corresponding author

Correspondence to Adriano Vogel .

Editor information

Editors and Affiliations

  1. Gesellschaft für Wissenschaftliche Datenverarbeitung mbH, Göttingen, Germany

    Ulrich Schwardmann

  2. Gesellschaft für Wissenschaftliche Datenverarbeitung mbH, Göttingen, Germany

    Christian Boehme

  3. CiTIUS, Santiago de Compostela, Spain

    Dora B. Heras

  4. University of Rome "Tor Vergata", Rome, Italy

    Valeria Cardellini

  5. Inria Bordeaux Sud-Ouest, Talence, France

    Emmanuel Jeannot

  6. Engineering Sardegna, Cagliari, Italy

    Antonio Salis

  7. University of Turin, Torino, Italy

    Claudio Schifanella

  8. University College Dublin, Dublin, Ireland

    Ravi Reddy Manumachu

  9. DLR-AS, Göttingen, Germany

    Dieter Schwamborn

  10. University of Pisa, Pisa, Italy

    Laura Ricci

  11. Ajou University, Suwon, Korea (Republic of)

    Oh Sangyoon

  12. RRZE Friedrich-Alexander-Universität, Erlangen, Germany

    Thomas Gruber

  13. ICAR-CNR, Napoli, Italy

    Laura Antonelli

  14. Tennessee Technological University, Cookeville, TN, USA

    Stephen L. Scott

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Vogel, A., Griebler, D., Danelutto, M., Fernandes, L.G. (2020). Minimizing Self-adaptation Overhead in Parallel Stream Processing for Multi-cores. In: Schwardmann, U., et al. Euro-Par 2019: Parallel Processing Workshops. Euro-Par 2019. Lecture Notes in Computer Science(), vol 11997. Springer, Cham. https://doi.org/10.1007/978-3-030-48340-1_3

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  • DOI: https://doi.org/10.1007/978-3-030-48340-1_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-48339-5

  • Online ISBN: 978-3-030-48340-1

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