IDEAS home Printed from https://ideas.repec.org/a/etm/ijsrst/v12y2025i4id1103.html

Reinforcement Learning for Efficient Resource Allocation in Cloud Computing: A Simulation Study

Author

Listed:
  • Alok Sharma
  • Ayan Rajput

Abstract

Cloud computing requires efficient resource allocation to ensure optimal utilization, performance, and cost reduction. Traditional methods are often insufficient to handle dynamic workloads. This paper presents a DRL approach using DQN to dynamically allocate cloud resources. A simulation environment models virtual machine provisioning and fluctuating workloads. The DQN agent learns resource allocation policies to maximize utilization and minimize operational cost and latency. Experimental results demonstrate improvements of 30% in CPU utilization, 40% reduction in latency, and 25% cost savings compared to static and threshold-based methods. The paper includes mathematical formulation, simulation data, and detailed analysis validating DRL’s effectiveness in cloud resource management.

Suggested Citation

  • Alok Sharma & Ayan Rajput, 2025. "Reinforcement Learning for Efficient Resource Allocation in Cloud Computing: A Simulation Study," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 12(4), pages 1000-1003, August.
    • Handle: RePEc:etm:ijsrst:v12:y2025:i4:id:1103
    DOI: 10.32628/IJSRST251381
    as

    Download full text from publisher

    File URL: https://ijsrst.com/home/article/view/IJSRST251381
    File Function: Abstract page
    Download Restriction: no
    File URL: https://ijsrst.com/home/article/download/IJSRST251381/IJSRST251381
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/IJSRST251381?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:etm:ijsrst:v12:y2025:i4:id:1103. 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: Pankaj Sharma (email available below). General contact details of provider: https://ijsrst.com/home .

    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.