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On Characterizing the Performance of Distributed Graph Computation Platforms

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Performance Characterization and Benchmarking. Traditional to Big Data (TPCTC 2014)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 8904))

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Abstract

Graphs are widely used for modeling complicated data in different application domains such as social networks, protein networks, transportation networks, bibliographical networks, knowledge bases and many more. Currently, graphs with millions and billions of nodes and edges have become very common. Therefore, designing scalable systems for processing and analyzing large scale graphs has become one of the most timely problems facing the big data research community. In practice, distributed processing of large scale graphs is a challenging task due to their size in addition to their inherent irregular structure and the iterative nature of graph processing and computation algorithms. In recent years, several distributed graph processing systems have been presented, most notably Pregel and GraphLab, to tackle this challenge. In particular, both systems use a vertex-centric computation model which enables the user to design a program that is executed locally for each vertex in parallel. In this paper, we analyze the performance characteristics of distributed graph processing systems and provide an experimental comparison on the performance of two popular systems in this area.

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Notes

  1. 1.

    http://www.insidefacebook.com/2012/10/04/facebook-reaches-billion-user- milestone/.

  2. 2.

    http://hadoop.apache.org/.

  3. 3.

    http://giraph.apache.org/.

  4. 4.

    http://hama.apache.org/.

  5. 5.

    https://github.com/jzachr/goldenorb.

  6. 6.

    http://zookeeper.apache.org/.

  7. 7.

    http://graphlab.org/.

  8. 8.

    http://snap.stanford.edu/data/web-Amazon.html.

  9. 9.

    https://giraph.apache.org/io.html.

  10. 10.

    http://hive.apache.org/.

  11. 11.

    http://hbase.apache.org/.

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Acknowledgment

This work was supported by King Abdulaziz City for Science and Technology (KACST) project 11-INF1990-03.

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Authors and Affiliations

  1. King Abdulaziz University, Jeddah, Saudi Arabia

    Ahmed Barnawi, Omar Batarfi & Ayman Fayoumi

  2. University of New South Wales, Sydney, Australia

    Seyed-Mehdi-Reza Behteshi, Reza Nouri & Sherif Sakr

  3. Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

    Radwa Elshawi

  4. King Saud Bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia

    Sherif Sakr

Authors
  1. Ahmed Barnawi
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  2. Omar Batarfi
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  3. Seyed-Mehdi-Reza Behteshi
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  4. Radwa Elshawi
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  5. Ayman Fayoumi
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  6. Reza Nouri
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  7. Sherif Sakr
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Corresponding author

Correspondence to Sherif Sakr .

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Editors and Affiliations

  1. Cisco Systems, Inc., San Jose, California, USA

    Raghunath Nambiar

  2. Oracle Corporation, Redwood Shores, California, USA

    Meikel Poess

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Barnawi, A. et al. (2015). On Characterizing the Performance of Distributed Graph Computation Platforms. In: Nambiar, R., Poess, M. (eds) Performance Characterization and Benchmarking. Traditional to Big Data. TPCTC 2014. Lecture Notes in Computer Science(), vol 8904. Springer, Cham. https://doi.org/10.1007/978-3-319-15350-6_3

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  • DOI: https://doi.org/10.1007/978-3-319-15350-6_3

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  • Print ISBN: 978-3-319-15349-0

  • Online ISBN: 978-3-319-15350-6

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