• DocumentCode
    3678559
  • Title

    The Status Prediction of Physical Machine in IaaS Cloud Environment

  • Author

    Qingxin Xia;Yuqing Lan;Limin Xiao

  • Author_Institution
    Sch. of Comput. Sci. &
  • fYear
    2015
  • Firstpage
    302
  • Lastpage
    305
  • Abstract
    At present, in researches of Iaas cloud resource scheduling strategies, it is focused that SLA violation or overloaded physical machine can trigger the migration of virtual machines, which will reduce the performance of the system and cause extra energy cost. In this paper, we model the resource of IaaS cloud based on Hidden Markov process to predict the status and the time that the physical machine is overloading, which will serve as a guideline for the resource scheduling in the IaaS cloud. Specifically, the resource status of physical machine will be chosen as the hidden status, meanwhile, the operations of virtual machine will be an observation set of the visible status, which are a modelling process. And then, we present the optimal path of the status transition probability as the core method of the physical machine status prediction. Finally, through real experimental scenarios, we verify the effectiveness of physical machine status prediction in the IaaS cloud environment.
  • Keywords
    "Hidden Markov models","Virtual machining","Prediction algorithms","Predictive models","Algorithm design and analysis","Probability distribution","Heuristic algorithms"
  • Publisher
    ieee
  • Conference_Titel
    Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2015 International Conference on
  • Type

    conf

  • DOI
    10.1109/CyberC.2015.100
  • Filename
    7307831