• DocumentCode
    3748376
  • Title

    Virtual machine placement based on the VM performance models in cloud

  • Author

    Hui Zhao;Qinghua Zheng; Weizhan Zhang;Yuxuan Chen; Yunhui Huang

  • Author_Institution
    SPKLSTN Lab, Department of Computer Science and Technology, Xi´an Jiaotong University, No.28, Xianning West Road, Shaanxi, 710049, China
  • fYear
    2015
  • Firstpage
    1
  • Lastpage
    8
  • Abstract
    Cloud service providers can offer users virtual machines (VMs) on-demand as a service over the Internet. VMs running on top of a physical machine (PM) share physical resources (CPU, memory, and/or bandwidth), and there may be a great resource contention among them, which results in VMs performance degradation. To prevent this, cloud providers need to study how to place VMs on PMs efficiently. However, the existing virtual machine placement (VMP) methods mainly tried to optimize the cloud resources instead of the VM performance. In this paper, we propose a VMP method based on the VM performance models in cloud. Firstly, with a real OpenStack cloud platform, we study the virtualization resource scheduling principle, analyze the interaction among VMs with shared hardware, consider the relationship between VMs and the host PM, and then we introduce the VM performance models to present the VM performance degradation problem. Secondly, to choose an appropriate PM for placing VM, we take into consideration the application-aware resource consumption characteristic, the VM resource requirement and the VM performance models, so as to minimize the PM performance degradation and guarantee the VM performance. Finally, we take the streaming media services for examples, and conduct some experiments to evaluate our method. The results show it works better than others and guarantees the VM performance significantly.
  • Keywords
    "Computational modeling","Streaming media","NASA","Computers","Silicon"
  • Publisher
    ieee
  • Conference_Titel
    Computing and Communications Conference (IPCCC), 2015 IEEE 34th International Performance
  • Electronic_ISBN
    2374-9628
  • Type

    conf

  • DOI
    10.1109/PCCC.2015.7410296
  • Filename
    7410296