IDEAS home Printed from https://ideas.repec.org/a/hin/jnlmpe/7848232.html
   My bibliography  Save this article

Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers

Author

Listed:
  • Xialin Liu
  • Junsheng Wu
  • Gang Sha
  • Shuqin Liu

Abstract

Cloud data centers consume huge amount of electrical energy bringing about in high operating costs and carbon dioxide emissions. Virtual machine (VM) consolidation utilizes live migration of virtual machines (VMs) to transfer a VM among physical servers in order to improve the utilization of resources and energy efficiency in cloud data centers. Most of the current VM consolidation approaches tend to aggressive-migrate for some types of applications such as large capacity application such as speech recognition, image processing, and decision support systems. These approaches generate a high migration thrashing because VMs are consolidated to servers according to VM’s instant resource usage without considering their overall and long-term utilization. The proposed approach, dynamic consolidation with minimization of migration thrashing (DCMMT) which prioritizes VM with high capacity, significantly reduces migration thrashing and the number of migrations to ensure service-level agreement (SLA) since it keeps VMs likely to suffer from migration thrashing in the same physical servers instead of migrating. We have performed experiments using real workload traces compared to existing aggressive-migration-based solutions; through simulations, we show that our approach improves migration thrashing metric by about 28%, number of migrations metric by about 21%, and SLAV metric by about 19%.

Suggested Citation

  • Xialin Liu & Junsheng Wu & Gang Sha & Shuqin Liu, 2020. "Virtual Machine Consolidation with Minimization of Migration Thrashing for Cloud Data Centers," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-13, August.
  • Handle: RePEc:hin:jnlmpe:7848232
    DOI: 10.1155/2020/7848232
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/MPE/2020/7848232.pdf
    Download Restriction: no

    File URL: http://downloads.hindawi.com/journals/MPE/2020/7848232.xml
    Download Restriction: no

    File URL: https://libkey.io/10.1155/2020/7848232?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

    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:hin:jnlmpe:7848232. 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: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.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.