IDEAS home Printed from https://ideas.repec.org/a/igg/jcac00/v12y2022i1p1-18.html
   My bibliography  Save this article

Efficient Resource Management Using Improved Bio-Inspired Algorithms for the Fog Computing Environment

Author

Listed:
  • Chetan M. Bulla

    (K.L.E. College of Engineering and Technology, India)

  • Mahantesh N. Birje

    (Visvesvaraya Technological University, India)

Abstract

The resource monitoring and management services together play a vital role in improving the overall performance of fog computing services. The monitoring system continuously keeps track of all resources by collecting and analyzing the status information and alert the user when the performance decreases. Resource management involves load balancing, resource scheduling and allocation and it requires accurate resource status which is provided by resource monitoring system to take scheduling and allocation decisions. The resource management activities are NP-hard problems and require optimal techniques to improve resource utilization and reduce energy consumption and latency. This paper proposes resource management model using improved bio-inspired algorithms and fog monitoring model to improve resource utilization and reduce energy consumption. The simulation results show that the proposed model is effective in terms of execution time, response time and energy consumption compared to the state of art techniques.

Suggested Citation

  • Chetan M. Bulla & Mahantesh N. Birje, 2022. "Efficient Resource Management Using Improved Bio-Inspired Algorithms for the Fog Computing Environment," International Journal of Cloud Applications and Computing (IJCAC), IGI Global, vol. 12(1), pages 1-18, January.
    • Handle: RePEc:igg:jcac00:v:12:y:2022:i:1:p:1-18
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJCAC.297104
    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:jcac00:v:12:y:2022:i:1:p:1-18. 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.