• DocumentCode
    2389020
  • Title

    Research on the performance of virtualization-based remote sensing data processing platform

  • Author

    Wang, Qi-Shuang ; Zhao, Dong ; Huang, Zhen-Chun

  • Author_Institution
    Dept. of Comput. Sci. & Technol., Tsinghua Nat. Lab. for Inf. Sci. & Technol. (TNLIST), Beijing, China
  • fYear
    2012
  • fDate
    19-20 May 2012
  • Firstpage
    900
  • Lastpage
    904
  • Abstract
    With the development of virtualization, a significant technology used in cloud computing, a new kind of remote sensing data processing platform based on virtualization has been formulating. In order to find out how virtual machines will influence the performance of remote sensing data processing platforms, KVM, VMware and Xen are tested as the instances of virtual machines by the drought model algorithm as a test application on remote sensing platforms. Besides, the test application is carefully studied and then divided into three procedures to find out which procedure is the main factor. In this paper, the results show that VMware and Xen deliver the superior to build the remote sensing data processing platforms, and that I/O performance, especially reading, is the obstacle to hinder the remote sensing application to migrate from Host Operating Systems to Guest Operating Systems.
  • Keywords
    cloud computing; data analysis; geophysics computing; operating systems (computers); remote sensing; virtual machines; virtualisation; I/O performance; KVM; VMware; Xen; cloud computing; drought model algorithm; guest operating system; host operating system; virtual machine; virtualization-based remote sensing data processing platform; Cloud computing; Data processing; Operating systems; Remote sensing; Virtual machine monitors; Virtual machining; Writing; performance; remote sensing; virtualization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Systems and Informatics (ICSAI), 2012 International Conference on
  • Conference_Location
    Yantai
  • Print_ISBN
    978-1-4673-0198-5
  • Type

    conf

  • DOI
    10.1109/ICSAI.2012.6223153
  • Filename
    6223153