Large scale graph processing systems: survey and an experimental evaluation

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• 作者：Omar Batarfi ; Radwa El Shawi ; Ayman G. Fayoumi ; Reza Nouri…
• 关键词：Big graph ; Graph processing ; Experimental evaluation
• 刊名：Cluster Computing
• 出版年：2015
• 出版时间：September 2015
• 年：2015
• 卷：18
• 期：3
• 页码：1189-1213
• 全文大小：5,612 KB
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• 作者单位：Omar Batarfi (1)
  Radwa El Shawi (2)
  Ayman G. Fayoumi (1)
  Reza Nouri (3)
  Seyed-Mehdi-Reza Beheshti (3)
  Ahmed Barnawi (1)
  Sherif Sakr (3) (4)
  
  1. King Abdulaziz University, Jeddah, Saudi Arabia
  2. Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
  3. University of New South Wales, Sydney, Australia
  4. King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia
  
• 刊物类别：Computer Science
• 刊物主题：Processor Architectures
  Operating Systems
  Computer Communication Networks
  
• 出版者：Springer Netherlands
• ISSN：1573-7543

文摘

Graph is a fundamental data structure that captures relationships between different data entities. In practice, 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. In principle, graph analytics is an important big data discovery technique. Therefore, with the increasing abundance of large graphs, 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 general, scalable processing of big graphs is a challenging task due to their size and the inherent irregular structure of graph computations. Thus, in recent years, we have witnessed an unprecedented interest in building big graph processing systems that attempted to tackle these challenges. In this article, we provide a comprehensive survey over the state-of-the-art of large scale graph processing platforms. In addition, we present an extensive experimental study of five popular systems in this domain, namely, GraphChi, Apache Giraph, GPS, GraphLab and GraphX. In particular, we report and analyze the performance characteristics of these systems using five common graph processing algorithms and seven large graph datasets. Finally, we identify a set of the current open research challenges and discuss some promising directions for future research in the domain of large scale graph processing. Keywords Big graph Graph processing Experimental evaluation