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Modeling Performance of Hadoop Applications: A Journey from Queueing Networks to Stochastic Well Formed Nets

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Algorithms and Architectures for Parallel Processing (ICA3PP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10048))

Abstract

Nowadays, many enterprises commit to the extraction of actionable knowledge from huge datasets as part of their core business activities. Applications belong to very different domains such as fraud detection or one-to-one marketing, and encompass business analytics and support to decision making in both private and public sectors. In these scenarios, a central place is held by the MapReduce framework and in particular its open source implementation, Apache Hadoop. In such environments, new challenges arise in the area of jobs performance prediction, with the needs to provide Service Level Agreement guarantees to the end-user and to avoid waste of computational resources. In this paper we provide performance analysis models to estimate MapReduce job execution times in Hadoop clusters governed by the YARN Capacity Scheduler. We propose models of increasing complexity and accuracy, ranging from queueing networks to stochastic well formed nets, able to estimate job performance under a number of scenarios of interest, including also unreliable resources. The accuracy of our models is evaluated by considering the TPC-DS industry benchmark running experiments on Amazon EC2 and the CINECA Italian supercomputing center. The results have shown that the average accuracy we can achieve is in the range 9–14%.

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Notes

  1. 1.

    http://www.hpc.cineca.it/hardware/pico.

  2. 2.

    http://www.tpc.org/tpcds/.

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Acknowledgments

This work has received funding from the European Union Horizon 2020 research and innovation program under grant agreement No. 644869 (DICE). Experimental data are available as open data at https://zenodo.org/record/58847#.V5i0wmXA45Q.

Author information

Authors and Affiliations

  1. Dip. di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy

    Danilo Ardagna, Eugenio Gianniti & Diego Perez-Palacin

  2. Centro Universitario de la Defensa, Academia General Militar, Zaragoza, Spain

    Simona Bernardi

  3. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

    Soroush Karimian Aliabadi

  4. Dpto. de Informática e Ingeniería de Sistemas, Universidad de Zaragoza, Zaragoza, Spain

    José Ignacio Requeno

Authors
  1. Danilo Ardagna
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  2. Simona Bernardi
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  3. Eugenio Gianniti
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  4. Soroush Karimian Aliabadi
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  5. Diego Perez-Palacin
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  6. José Ignacio Requeno
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Corresponding author

Correspondence to Danilo Ardagna .

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Editors and Affiliations

  1. University Carlos III of Madrid, Leganes, Spain

    Jesus Carretero

  2. Carlos III University of Madrid, Leganes, Madrid, Spain

    Javier Garcia-Blas

  3. The University of Waikato, Hamilton, New Zealand

    Ryan K.L. Ko

  4. IBM Zurich Research Laboratory, Rüschlikon, Switzerland

    Peter Mueller

  5. Hiroshima University, Higashi-Hiroshima, Japan

    Koji Nakano

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Ardagna, D., Bernardi, S., Gianniti, E., Karimian Aliabadi, S., Perez-Palacin, D., Requeno, J.I. (2016). Modeling Performance of Hadoop Applications: A Journey from Queueing Networks to Stochastic Well Formed Nets. In: Carretero, J., Garcia-Blas, J., Ko, R., Mueller, P., Nakano, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2016. Lecture Notes in Computer Science(), vol 10048. Springer, Cham. https://doi.org/10.1007/978-3-319-49583-5_47

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  • DOI: https://doi.org/10.1007/978-3-319-49583-5_47

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  • Online ISBN: 978-3-319-49583-5

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