IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v11y2025i2id1311.html

AI-Driven Infrastructure Scaling for Cost Optimization in Cloud Environments: A Systematic Review

Author

Listed:
  • Prasen Reddy Yakkanti

Abstract

This article comprehensively analyzes AI-driven infrastructure scaling for cost optimization in cloud environments. We examine how machine learning algorithms can dynamically adjust cloud resources based on historical patterns and real-time workload demands, addressing the persistent challenge of balancing performance requirements with cost efficiency. The article analyzes various scaling mechanisms, including historical pattern analysis, real-time monitoring systems, and decision-making algorithms for resource adjustment, alongside predictive analytics approaches for workload forecasting. Through multiple case studies across diverse industry sectors, the article identifies best practices, implementation challenges, and integration considerations for organizations adopting these technologies. The article also explores emerging directions, including serverless architecture integration, multi-cloud optimization strategies, and edge computing applications. The article's findings indicate that AI-driven infrastructure scaling represents a significant advancement in cloud resource management, enabling organizations to optimize their cloud expenditure while maintaining application reliability and performance.

Suggested Citation

  • Prasen Reddy Yakkanti, 2025. "AI-Driven Infrastructure Scaling for Cost Optimization in Cloud Environments: A Systematic Review," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 11(2), pages 2685-2693, March.
    • Handle: RePEc:jbh:ijsrcs:v11:y2025:i2:id:1311
    DOI: 10.32628/CSEIT25112741
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT25112741
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT25112741
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT25112741/CSEIT25112741
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT25112741?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:jbh:ijsrcs:v11:y2025:i2:id:1311. 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 (USA) (email available below). General contact details of provider: https://ijsrcseit.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.