IDEAS home Printed from https://ideas.repec.org/a/spr/infosf/v16y2014i1d10.1007_s10796-013-9459-0.html
   My bibliography  Save this article

A cost-aware auto-scaling approach using the workload prediction in service clouds

Author

Listed:
  • Jingqi Yang

    (Beijing University of Posts & Telecommunications)

  • Chuanchang Liu

    (Beijing University of Posts & Telecommunications)

  • Yanlei Shang

    (Beijing University of Posts & Telecommunications)

  • Bo Cheng

    (Beijing University of Posts & Telecommunications)

  • Zexiang Mao

    (Beijing University of Posts & Telecommunications)

  • Chunhong Liu

    (Beijing University of Posts & Telecommunications)

  • Lisha Niu

    (Beijing University of Posts & Telecommunications)

  • Junliang Chen

    (Beijing University of Posts & Telecommunications)

Abstract

Service clouds are distributed infrastructures which deploys communication services in clouds. The scalability is an important characteristic of service clouds. With the scalability, the service cloud can offer on-demand computing power and storage capacities to different services. In order to achieve the scalability, we need to know when and how to scale virtual resources assigned to different services. In this paper, a novel service cloud architecture is presented, and a linear regression model is used to predict the workload. Based on this predicted workload, an auto-scaling mechanism is proposed to scale virtual resources at different resource levels in service clouds. The auto-scaling mechanism combines the real-time scaling and the pre-scaling. Finally experimental results are provided to demonstrate that our approach can satisfy the user Service Level Agreement (SLA) while keeping scaling costs low.

Suggested Citation

  • Jingqi Yang & Chuanchang Liu & Yanlei Shang & Bo Cheng & Zexiang Mao & Chunhong Liu & Lisha Niu & Junliang Chen, 2014. "A cost-aware auto-scaling approach using the workload prediction in service clouds," Information Systems Frontiers, Springer, vol. 16(1), pages 7-18, March.
    • Handle: RePEc:spr:infosf:v:16:y:2014:i:1:d:10.1007_s10796-013-9459-0
    DOI: 10.1007/s10796-013-9459-0
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10796-013-9459-0
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10796-013-9459-0?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
    ---><---

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

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Kena Alexander & Muhammad Hanif & Choonhwa Lee & Eunsam Kim & Sumi Helal, 2020. "Cost-aware orchestration of applications over heterogeneous clouds," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-21, February.
    2. Ching-Hsien Hsu & Jianhua Ma & Mohammad S. Obaidat, 2014. "Dynamic intelligence towards merging cloud and communication services," Information Systems Frontiers, Springer, vol. 16(1), pages 1-5, March.
    3. Shuai Yuan & Sanjukta Das & Ram Ramesh & Chunming Qiao, 2023. "Availability-Aware Virtual Resource Provisioning for Infrastructure Service Agreements in the Cloud," Information Systems Frontiers, Springer, vol. 25(4), pages 1495-1512, August.

    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:spr:infosf:v:16:y:2014:i:1:d:10.1007_s10796-013-9459-0. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.