• DocumentCode
    258220
  • Title

    Providing IaaS resources automatically through prediction and monitoring approaches

  • Author

    da Silva Dias, Ariel ; Nakamura, Luis H. V. ; Estrella, Julio C. ; Santana, Regina H. C. ; Santana, Marcos J.

  • Author_Institution
    Inst. of Math. & Comput. Sci., Univ. of Sao Paulo, São Carlos, Brazil
  • fYear
    2014
  • fDate
    23-26 June 2014
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    A cloud computing infrastructure management is proposed in this paper, which consists of two approaches that facilitate the provisioning of computing resources in a self-adaptive virtualized environment. Resource allocation is employed to predict the future of workload management and to employ a self-adaptive approach by using computational agents to monitor the Virtual Machines (VMs). The paper also includes the Return on Investment (ROI) formula that deals with the relationship between the prices for the Infrastructure-as-a-Service (IaaS) contracted by the customer and the effective use of this service. The experimental results show a significant improvement when self-configuration is used with agent-based computational modeling in contrast with the self-configuration based on prediction for future workload.
  • Keywords
    cloud computing; cost-benefit analysis; investment; resource allocation; virtual machines; IaaS resources; ROI formula; VMs; agent-based computational modeling; cloud computing infrastructure management; infrastructure-as-a-service; monitoring approaches; prices; resource allocation; return on investment; self-adaptive approach; self-adaptive virtualized environment; virtual machines; workload management prediction approach; Contracts; Investment; Market research; Monitoring; Servers; Time series analysis; Web services; Cloud Computing; Performance; Return on Investment; Workload Prediction;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computers and Communication (ISCC), 2014 IEEE Symposium on
  • Conference_Location
    Funchal
  • Type

    conf

  • DOI
    10.1109/ISCC.2014.6912590
  • Filename
    6912590