IDEAS home Printed from https://ideas.repec.org/a/alk/jcsts0/v5y2023i2id5275.html

Enhancing Load Balancing in Cloud Computing through Adaptive Task Prioritization

Author

Listed:
  • Hieu Le Ngoc

    (Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam)

  • Hung Tran Cong

    (Posts and Telecommunication Institute of Technology, Ho Chi Minh City, Vietnam)

Abstract

Cloud computing has become an increasingly popular platform for modern applications and daily life, and one of its greatest challenges is task scheduling and allocation. Numerous studies have shown that the performance of cloud computing systems relies heavily on arranging tasks in the execution stream on cloud hosts, which is managed by the cloud's load balancer. In this paper, we investigate task priority based on user behavior using request properties and propose an algorithm that utilizes machine learning techniques, namely k-NN and Regression, to classify task-based priorities of requests, facilitate proper allocation, and scheduling of tasks. We aim to enhance load balancing in the cloud by incorporating external factors of the load balancer. The proposed algorithm is experimentally tested on the CloudSim environment, demonstrating improved load balancer performance compared to other popular LB algorithms.

Suggested Citation

  • Hieu Le Ngoc & Hung Tran Cong, 2023. "Enhancing Load Balancing in Cloud Computing through Adaptive Task Prioritization," Journal of Computer Science and Technology Studies, Al-Kindi Center for Research and Development, vol. 5(2), pages 01-15, April.
    • Handle: RePEc:alk:jcsts0:v:5:y:2023:i:2:id:5275
    DOI: 10.32996/jcsts.2023.5.2.1
    as

    Download full text from publisher

    File URL: https://al-kindipublisher.com/index.php/jcsts/article/view/5275
    File Function: Abstract page
    Download Restriction: no
    File URL: https://al-kindipublisher.com/index.php/jcsts/article/download/5275/4438
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32996/jcsts.2023.5.2.1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    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:alk:jcsts0:v:5:y:2023:i:2:id:5275. 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: Al-Kindi Center for Research and Development (email available below). General contact details of provider: https://al-kindipublisher.com/index.php/jcsts .

    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.