IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v9y2017i4p87-d120674.html
   My bibliography  Save this article

High-Performance Elastic Management for Cloud Containers Based on Predictive Message Scheduling

Author

Listed:
  • Chengxin Yan

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

  • Ningjiang Chen

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

  • Zhang Shuo

    (School of Computer and Electronic Information, Guangxi University, Nanning 530004, China)

Abstract

Containerized data centers can improve the computational density of IaaS layers. This intensive high-concurrency environment has high requirements for message scheduling and container processing. In the paper, an elastically scalable strategy for cloud containers based on predictive message scheduling is introduced, in order to reduce the delay of messages and improve the response time of services and the utilization of container resources. According to the busy degree of different containers, a management strategy of multiple containers at message-granularity level is developed, which gives the containers better elasticity. The simulation results show that the proposed strategy improves service processing efficiency and reduces response latency compared with existing solutions.

Suggested Citation

  • Chengxin Yan & Ningjiang Chen & Zhang Shuo, 2017. "High-Performance Elastic Management for Cloud Containers Based on Predictive Message Scheduling," Future Internet, MDPI, vol. 9(4), pages 1-13, November.
    • Handle: RePEc:gam:jftint:v:9:y:2017:i:4:p:87-:d:120674
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/9/4/87/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/9/4/87/
    Download Restriction: no
    ---><---

    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:gam:jftint:v:9:y:2017:i:4:p:87-:d:120674. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.