• DocumentCode
    3678407
  • Title

    GRAPH/Z: A Key-Value Store Based Scalable Graph Processing System

  • Author

    Tonglin Li; Chaoqi Ma; Jiabao Li; Xiaobing Zhou; Ke Wang; Dongfang Zhao;Iman Sadooghi;Ioan Raicu

  • fYear
    2015
  • Firstpage
    516
  • Lastpage
    517
  • Abstract
    The emerging applications in big data and social networks issue rapidly increasing demands on graph processing. Graph query operations that involve a large number of vertices and edges can be tremendously slow on traditional databases. The state-of-the-art graph processing systems and databases usually adopt master/slave architecture that potentially impairs their The contributions of this paper are as follows: scalability. This work describes the design and implementation of a new graph processing system based on Bulk Synchronous Parallel model. Our system is built on top of ZHT, a scalable distributed key-value store, which benefits the graph processing in terms of scalability, performance and persistency. The experiment results imply excellent scalability.
  • Keywords
    "Yttrium","Conferences","Big data","Computational modeling","Loading","Scalability","Databases"
  • Publisher
    ieee
  • Conference_Titel
    Cluster Computing (CLUSTER), 2015 IEEE International Conference on
  • Type

    conf

  • DOI
    10.1109/CLUSTER.2015.90
  • Filename
    7307637