IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v9y2017i3p37-d105107.html
   My bibliography  Save this article

A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine

Author

Listed:
  • Zhou Lei

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Exiong Sun

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Shengbo Chen

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Jiang Wu

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

  • Wenfeng Shen

    (School of Computer Engineering and Science, Shanghai University, Shanghai 200444, China)

Abstract

Live migration of virtual machines is an important approach for dynamic resource scheduling in cloud environment. The hybrid-copy algorithm is an excellent algorithm that combines the pre-copy algorithm with the post-copy algorithm to remedy the defects of the pre-copy algorithm and the post-copy algorithm. Currently, the hybrid-copy algorithm only copies all memory pages once in advance. In a write-intensive workload, copy memory pages once may be enough. However, more iterative copy rounds can significantly reduce the page faults in a read-intensive workload. In this paper, we propose a new parameter to decide the appropriate time to stop the iterative copy phase based on real-time situation. We use a Markov model to forecast the memory access pattern. Based on the predicted results and the analysis of the actual situation, the memory page transfer order would be adjusted to reduce the invalid transfers. The novel hybrid-copy algorithm is implemented on the Xen platform. The experimental results demonstrate that our mechanism has good performance both on read-intensive workloads and write-intensive workloads.

Suggested Citation

  • Zhou Lei & Exiong Sun & Shengbo Chen & Jiang Wu & Wenfeng Shen, 2017. "A Novel Hybrid-Copy Algorithm for Live Migration of Virtual Machine," Future Internet, MDPI, vol. 9(3), pages 1-13, July.
    • Handle: RePEc:gam:jftint:v:9:y:2017:i:3:p:37-:d:105107
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/9/3/37/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/9/3/37/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Moiz Arif & Adnan K. Kiani & Junaid Qadir, 2017. "Machine learning based optimized live virtual machine migration over WAN links," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 64(2), pages 245-257, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cihan Şahin, 2023. "Predicting base station return on investment in the telecommunications industry: Machine‐learning approaches," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(1), pages 29-40, January.

    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:3:p:37-:d:105107. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.