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Simplifying and implementing service level objectives for stream parallelism

  • Dalvan GrieblerEmail author
  • Adriano Vogel
  • Daniele De Sensi
  • Marco Danelutto
  • Luiz G. Fernandes
Article

Abstract

An increasing attention has been given to provide service level objectives (SLOs) in stream processing applications due to the performance and energy requirements, and because of the need to impose limits in terms of resource usage while improving the system utilization. Since the current and next-generation computing systems are intrinsically offering parallel architectures, the software has to naturally exploit the architecture parallelism. Implement and meet SLOs on existing applications is not a trivial task for application programmers, since the software development process, besides the parallelism exploitation, requires the implementation of autonomic algorithms or strategies. This is a system-oriented programming approach and requires the management of multiple knobs and sensors (e.g., the number of threads to use, the clock frequency of the cores, etc.) so that the system can self-adapt at runtime. In this work, we introduce a new and simpler way to define SLO in the application’s source code, by abstracting from the programmer all the details relative to self-adaptive system implementation. The application programmer specifies which parts of the code to parallelize and the related SLOs that should be enforced. To reach this goal, source-to-source code transformation rules are implemented in our compiler, which automatically generates self-adaptive strategies to enforce, at runtime, the user-expressed objectives. The experiments highlighted promising results with simpler, effective, and efficient SLO implementations for real-world applications.

Keywords

Parallel programming Stream processing Self-adaptive Domain-specific language Power-aware computing 

Notes

Acknowledgements

This study was partially funded by the Coordenação de Aperfeiço-amento de Pessoal de Nivel Superior-Brasil (CAPES)-Finance Code 001 and by the FAPERGS 01/2017-ARD Project ParaElastic (No. 17/2551-0000871-5). We would like to thank Laboratório de Alto Desempenho (LAD) from PUCRS for partially providing computing resources.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of TechnologyPontifical Catholic University of Rio Grande do Sul (PUCRS)Porto AlegreBrazil
  2. 2.Department of Computer ScienceUniversity of Pisa (UNIPI)PisaItaly
  3. 3.Laboratory of Advanced Research on Cloud Computing (LARCC)Três de Maio Faculty (SETREM)Três de MaioBrazil

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