IDEAS home Printed from https://ideas.repec.org/a/ids/ijnvor/v26y2022i1-2p3-22.html
   My bibliography  Save this article

A cluster workload forecasting strategy using a higher order statistics based ARMA model for IaaS cloud services

Author

Listed:
  • Zohra Amekraz
  • Moulay Youssef Hadi

Abstract

With cloud services becoming more popular among internet users, cloud providers are facing a challenge in allocating resources according to users' demand instantly due to the delay caused by the virtual machines' start up time. This problem can be solved using proactive allocation techniques that predict the workload in advance and make scaling decisions ahead of time. In this paper, we present an adaptive workload prediction method based on higher order statistics (HOS) and autoregressive moving average (ARMA) model. We use HOS to make a Gaussianity checking test of the cloud workload and decide the suitable identification method of the ARMA model to be used for forecasting. We evaluate our proposal with two real traces extracted from cluster workloads. The results show that the proposed method has an average of 34% higher accuracy than the baseline ARMA model and presents a low forecasting overhead (< 2 s).

Suggested Citation

  • Zohra Amekraz & Moulay Youssef Hadi, 2022. "A cluster workload forecasting strategy using a higher order statistics based ARMA model for IaaS cloud services," International Journal of Networking and Virtual Organisations, Inderscience Enterprises Ltd, vol. 26(1/2), pages 3-22.
    • Handle: RePEc:ids:ijnvor:v:26:y:2022:i:1/2:p:3-22
    as

    Download full text from publisher

    File URL: http://www.inderscience.com/link.php?id=121844
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    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:ids:ijnvor:v:26:y:2022:i:1/2:p:3-22. 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: Sarah Parker (email available below). General contact details of provider: http://www.inderscience.com/browse/index.php?journalID=22 .

    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.