Performance Characterization and Benchmarking. Traditional to Big Data

Volume 8904 of the series Lecture Notes in Computer Science pp 29-43


On Characterizing the Performance of Distributed Graph Computation Platforms

  • Ahmed BarnawiAffiliated withKing Abdulaziz University
  • , Omar BatarfiAffiliated withKing Abdulaziz University
  • , Seyed-Mehdi-Reza BehteshiAffiliated withUniversity of New South Wales
  • , Radwa ElshawiAffiliated withPrincess Nourah Bint Abdulrahman University
  • , Ayman FayoumiAffiliated withKing Abdulaziz University
  • , Reza NouriAffiliated withUniversity of New South Wales
  • , Sherif SakrAffiliated withUniversity of New South WalesKing Saud Bin Abdulaziz University for Health Sciences Email author 

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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.