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High-Performance Graph Data Management and Mining in Cloud Environments with X10

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Cloud Computing

Part of the book series: Computer Communications and Networks ((CCN))

Abstract

Large-scale graph data management and mining in cloud environments have been a widely discussed issue in recent times. The goal and the scope of this chapter is to discuss how X10 (a Partitioned Global Address Space (PGAS) language) has been applied for programming data-intensive systems. Specifically, we focus on the problem of creating scalable systems for storing and processing large-scale graph data on HPC clouds with X10. The chapter first discusses about large-scale graph processing with X10. Next, it describes the experience of designing and implementing a distributed graph database engine called Acacia with X10. We specifically focus on Acacia’s RDF extension. Finally, it will describe how a graph database benchmarking framework called XGDBench has been developed to analyze the performance of graph database servers. Overall the chapter describes our experiences of implementing such graph-based systems and frameworks with X10.

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Authors and Affiliations

  1. WSO2, Inc., Mountain View, CA, USA

    Miyuru Dayarathna

  2. University of Moratuwa, Moratuwa, Sri Lanka

    Miyuru Dayarathna

  3. T.J. Watson Research Center, IBM, New York, NY, USA

    Toyotaro Suzumura

  4. Barcelona Supercomputing Center, Barcelona, Spain

    Toyotaro Suzumura

  5. University of Tokyo, Tokyo, Japan

    Toyotaro Suzumura

Authors
  1. Miyuru Dayarathna
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Correspondence to Miyuru Dayarathna .

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Editors and Affiliations

  1. University of Derby, Derby, Derbyshire, United Kingdom

    Nick Antonopoulos

  2. University of Surrey, Guildford, Surrey, United Kingdom

    Lee Gillam

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Dayarathna, M., Suzumura, T. (2017). High-Performance Graph Data Management and Mining in Cloud Environments with X10. In: Antonopoulos, N., Gillam, L. (eds) Cloud Computing. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-54645-2_7

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