• DocumentCode
    3231340
  • Title

    An Approach for Dynamic Scaling of Resources in Enterprise Cloud

  • Author

    Kanagala, K. ; Sekaran, K. Chandra

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Nat. Inst. of Technol. Karnataka, Surathkal, India
  • Volume
    2
  • fYear
    2013
  • fDate
    2-5 Dec. 2013
  • Firstpage
    345
  • Lastpage
    348
  • Abstract
    Elasticity is one of the key governing properties of cloud computing that has major effects on cost and performance directly. Most of the popular Infrastructure as a Service (IaaS) providers such as Amazon Web Services (AWS), Windows Azure, Rack space etc. work on threshold-based auto-scaling. In current IaaS environments there are various other factors like "Virtual Machine (VM)-turnaround time", "VM-stabilization time" etc. that affect the newly started VM from start time to request servicing time. If these factors are not considered while auto-scaling, then they will have direct effect on Service Level Agreement (SLA) implementations and users\´ response time. Therefore, these thresholds should be a function of load trend, which makes VM readily available when needed. Hence, we developed an approach where the thresholds adapt in advance and these thresholds are functions of all the above mentioned factors. Our experimental results show that our approach gives the better response time.
  • Keywords
    cloud computing; virtual machines; AWS; Amazon Web Services; IaaS provider; Infrastructure as a service; Rack space; SLA; VM-stabilization time; VM-turnaround time; Windows Azure; cloud computing; dynamic scaling; enterprise cloud; load trend; service level agreement; threshold-based autoscaling; virtual machine; Cloud computing; Conferences; Forecasting; Market research; Monitoring; Smoothing methods; Time factors; Auto-scale; Cloud; DES; IaaS; SLA; Virtual Machines;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud Computing Technology and Science (CloudCom), 2013 IEEE 5th International Conference on
  • Conference_Location
    Bristol
  • Type

    conf

  • DOI
    10.1109/CloudCom.2013.167
  • Filename
    6735449