• DocumentCode
    650601
  • Title

    VScaler: Autonomic Virtual Machine Scaling

  • Author

    Yazdanov, Lenar ; Fetzer, Christof

  • Author_Institution
    Fac. of Comput. Sci., Tech. Univ. Dresden, Dresden, Germany
  • fYear
    2013
  • fDate
    June 28 2013-July 3 2013
  • Firstpage
    212
  • Lastpage
    219
  • Abstract
    Recent research results in cloud community found that cloud users increasingly force providers to shift from fixed bundle instance types(e.g. Amazon instances) to flexible bundles and shrinked billing cycles. This means that cloud applications can dynamically provision the used amount of resources in a more fine-grained fashion. This observation calls for approaches which are able to automatically implement fine granular VM resource allocation with respect to user-provided SLAs. In this work we propose VScaler, a framework which implements autonomic resource allocation using a novel approach to reinforcement learning.
  • Keywords
    cloud computing; contracts; learning (artificial intelligence); resource allocation; user interfaces; virtual machines; VScaler; autonomic virtual machine scaling; billing cycles; cloud community; fine-grained fashion; reinforcement learning; resource allocation; user-provided SLA; Adaptation models; Cloud computing; History; Learning (artificial intelligence); Prediction algorithms; Random access memory; Resource management; measurement; performance; scalability;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud Computing (CLOUD), 2013 IEEE Sixth International Conference on
  • Conference_Location
    Santa Clara, CA
  • Print_ISBN
    978-0-7695-5028-2
  • Type

    conf

  • DOI
    10.1109/CLOUD.2013.142
  • Filename
    6676697