• DocumentCode
    1925983
  • Title

    Granules: A lightweight, streaming runtime for cloud computing with support, for Map-Reduce

  • Author

    Pallickara, Shrideep ; Ekanayake, Jaliya ; Fox, Geoffrey

  • fYear
    2009
  • fDate
    Aug. 31 2009-Sept. 4 2009
  • Firstpage
    1
  • Lastpage
    10
  • Abstract
    Cloud computing has gained significant traction in recent years. The Map-Reduce framework is currently the most dominant programming model in cloud computing settings. In this paper, we describe Granules, a lightweight, streaming-based runtime for cloud computing which incorporates support for the Map-Reduce framework. Granules provides rich lifecycle support for developing scientific applications with support for iterative, periodic and data driven semantics for individual computations and pipelines. We describe our support for variants of the Map-Reduce framework. The paper presents a survey of related work in this area. Finally, this paper describes our performance evaluation of various aspects of the system, including (where possible) comparisons with other comparable systems.
  • Keywords
    graph theory; pipeline processing; program diagnostics; Granules; Map-Reduce; cloud computing; data driven semantics; Application software; Cloud computing; Computer science; Concurrent computing; Hardware; Machine learning algorithms; Parallel processing; Parallel programming; Pipelines; Runtime; cloud computing; cloud runtimes; content distribution networks; map-reduce; streaming;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cluster Computing and Workshops, 2009. CLUSTER '09. IEEE International Conference on
  • Conference_Location
    New Orleans, LA
  • ISSN
    1552-5244
  • Print_ISBN
    978-1-4244-5011-4
  • Electronic_ISBN
    1552-5244
  • Type

    conf

  • DOI
    10.1109/CLUSTR.2009.5289160
  • Filename
    5289160