• DocumentCode
    173033
  • Title

    Virtual Numbers for Virtual Machines?

  • Author

    Tan, Alan Y. S. ; Ko, Ryan K. L. ; Mendiratta, Veena

  • Author_Institution
    Dept. of Comput. Sci., Univ. of Waikato, Hamilton, New Zealand
  • fYear
    2014
  • fDate
    June 27 2014-July 2 2014
  • Firstpage
    972
  • Lastpage
    974
  • Abstract
    Knowing the number of virtual machines (VMs) that a cloud physical hardware can (further) support is critical as it has implications on provisioning and hardware procurement. However, current methods for estimating the maximum number of VMs possible on a given hardware is usually the ratio of the specifications of a VM to the underlying cloud hardware´s specifications. Such naive and linear estimation methods mostly yield impractical limits as to how many VMs the hardware can actually support. It was found that if we base on the naive division method, user experience on VMs at those limits would be severely degraded. In this paper, we demonstrate through experimental results, the significant gap between the limits derived using the estimation method mentioned above and the actual situation. We believe for a more practicable estimation of the limits of the underlying infrastructure, dominant workload of VMs should also be factored in.
  • Keywords
    cloud computing; virtual machines; VM specifications; cloud computing; cloud hardware specifications; cloud physical hardware; cloud resource provisioning; hardware procurement; linear estimation methods; naive division method; virtual machines; virtual numbers; virtualization; Cloud computing; Estimation; Hardware; Random access memory; Servers; Usability; Virtual machining; cloud computing; cloud resource provisioning; load limit prediction; virtualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud Computing (CLOUD), 2014 IEEE 7th International Conference on
  • Conference_Location
    Anchorage, AK
  • Print_ISBN
    978-1-4799-5062-1
  • Type

    conf

  • DOI
    10.1109/CLOUD.2014.147
  • Filename
    6973853