IDEAS home Printed from https://ideas.repec.org/a/igg/jismd0/v11y2020i4p18-35.html
   My bibliography  Save this article

An Analysis on Task Migration Strategy of Big Data Streaming Storm Computing Framework for Distributed Processing

Author

Listed:
  • Xiling Hu

    (Jiangxi Vocational College of Finance and Economics, China)

Abstract

In this modern era, a large volume of data is generated regularly, which needs to be processed for gaining profits of latent information. The processing of big data is composed of a challenge termed as communication overhead. In order to minimize communication overhead on the premise of various resource constraints, a task migration strategy under heterogeneous storm environment is proposed. The proposed strategy is based on the establishment and demonstration of storm resource constraint model, optimal communication overhead model, and task migration model. Where the source node selection algorithm adds the nodes beyond the threshold to the source node set according to the load of CPU, memory, and network bandwidth of each working node in the cluster and the priority order of various resources. The experiment shows that compared with the existing research, computing storm can effectively reduce the delay and communication overhead between nodes, and the execution overhead is small.

Suggested Citation

  • Xiling Hu, 2020. "An Analysis on Task Migration Strategy of Big Data Streaming Storm Computing Framework for Distributed Processing," International Journal of Information System Modeling and Design (IJISMD), IGI Global, vol. 11(4), pages 18-35, October.
    • Handle: RePEc:igg:jismd0:v:11:y:2020:i:4:p:18-35
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJISMD.2020100102
    Download Restriction: no
    ---><---

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jismd0:v:11:y:2020:i:4:p:18-35. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.