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Autonomic and Latency-Aware Degree of Parallelism Management in SPar

  • Adriano VogelEmail author
  • Dalvan Griebler
  • Daniele De Sensi
  • Marco Danelutto
  • Luiz Gustavo Fernandes
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11339)

Abstract

Stream processing applications became a representative workload in current computing systems. A significant part of these applications demands parallelism to increase performance. However, programmers are often facing a trade-off between coding productivity and performance when introducing parallelism. SPar was created for balancing this trade-off to the application programmers by using the C++11 attributes’ annotation mechanism. In SPar and other programming frameworks for stream processing applications, the manual definition of the number of replicas to be used for the stream operators is a challenge. In addition to that, low latency is required by several stream processing applications. We noted that explicit latency requirements are poorly considered on the state-of-the-art parallel programming frameworks. Since there is a direct relationship between the number of replicas and the latency of the application, in this work we propose an autonomic and adaptive strategy to choose the proper number of replicas in SPar to address latency constraints. We experimentally evaluated our implemented strategy and demonstrated its effectiveness on a real-world application, demonstrating that our adaptive strategy can provide higher abstraction levels while automatically managing the latency.

Keywords

Autonomic computing Stream processing Parallel programming Adaptive degree of parallelism 

Notes

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nivel Superior - Brasil (CAPES) - Finance Code 001, by the EU H2020-ICT-2014-1 project RePhrase (No. 644235), and by the FAPERGS 01/2017-ARD project ParaElastic (No. 17/2551-0000871-5).

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Adriano Vogel
    • 1
    Email author
  • Dalvan Griebler
    • 1
    • 3
  • Daniele De Sensi
    • 2
  • Marco Danelutto
    • 2
  • Luiz Gustavo Fernandes
    • 1
  1. 1.School of TechnologyPontifical Catholic University of Rio Grande do SulPorto AlegreBrazil
  2. 2.Department of Computer ScienceUniversity of PisaPisaItaly
  3. 3.Laboratory of Advanced Research on Cloud ComputingTrês de Maio FacultyTrês de MaioBrazil

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