IDEAS home Printed from https://ideas.repec.org/a/jbh/ijsrcs/v10y2024i2id134.html

Improving Amazon EC2 Spot Instances Price Prediction using Machine Learning Algorithm

Author

Listed:
  • M. Prasanthi
  • G.Chishma
  • P. Padmavathi
  • K. Reethika
  • A. Chandra Sekhar

Abstract

Spot instances were introduced by Amazon EC2 in December 2009 to sell its spare capacity through auction based market mechanism. Despite its extremely low prices, cloud spot market has low utilization. Spot pricing being dynamic, spot instances are prone to out-of bid failure. Bidding complexity is another reason why users today still fear using spot instances. This work aims to present Regression Random Forests (RRFs) model to predict one-week-ahead and one-day-ahead spot prices. The prediction would assist cloud users to plan in advance when to acquire spot instances, estimate execution costs, and also assist them in bid decision making to minimize execution costs and out-of-bid failure probability. Simulations with 12 months real Amazon EC2 spot history traces to forecast future spot prices show the effectiveness of the proposed technique. Comparison of RRFs based spot price forecasts with existing non-parametric machine learning models reveal that RRFs based forecast accuracy outperforms other models. We measure predictive accuracy using MAPE, MCPE, OOBError and speed. Evaluation results show that

Suggested Citation

  • M. Prasanthi & G.Chishma & P. Padmavathi & K. Reethika & A. Chandra Sekhar, 2024. "Improving Amazon EC2 Spot Instances Price Prediction using Machine Learning Algorithm," 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. 10(2), pages 713-720, April.
    • Handle: RePEc:jbh:ijsrcs:v10:y2024:i2:id:134
    DOI: 10.32628/CSEIT24102102
    Note: Article URL: https://ijsrcseit.com/home/article/view/CSEIT24102102
    as

    Download full text from publisher

    File URL: https://ijsrcseit.com/home/article/view/CSEIT24102102
    File Function: Article URL
    Download Restriction: no
    File URL: https://ijsrcseit.com/home/article/download/CSEIT24102102/CSEIT24102102
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.32628/CSEIT24102102?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

    ;
    ;
    ;
    ;
    ;
    ;
    ;

    JEL classification:

    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:v10:y2024:i2:id:134. 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.