• DocumentCode
    2266068
  • Title

    Integrated and autonomic cloud resource scaling

  • Author

    Hasan, Masum Z. ; Magana, Edgar ; Clemm, Alexander ; Tucker, Lew ; Gudreddi, Sree Lakshmi D

  • Author_Institution
    Cisco Syst., San Jose, CA, USA
  • fYear
    2012
  • fDate
    16-20 April 2012
  • Firstpage
    1327
  • Lastpage
    1334
  • Abstract
    A Cloud is a very dynamic environment where resources offered by a Cloud Service Provider (CSP), out of one or more Cloud Data Centers (DCs) are acquired or released (by an enterprise (tenant) on-demand and at any scale. Typically a tenant will use Cloud service interfaces to acquire or release resources directly. This process can be automated by a CSP by providing auto-scaling capability where a tenant sets policies indicating under what condition resources should be auto-scaled. This is specially needed in a Cloud environment because of the huge scale at which a Cloud operates. Typical solutions are naïve causing spurious auto-scaling decisions. For example, they are based on only thresholding triggers and the thresholding mechanisms themselves are not Cloud-ready. In a Cloud, resources from three separate domains, compute, storage and network, are acquired or released on-demand. But in typical solutions resources from these three domains are not auto-scaled in an integrated fashion. Integrated auto-scaling prevents further spurious scaling and reduces the number of auto-scaling systems to be supported in a Cloud management system. In addition, network resources typically are not auto-scaled. In this paper we describe a Cloud resource auto-scaling system that addresses and overcomes above limitations.
  • Keywords
    cloud computing; resource allocation; auto-scaling capability; cloud data center; cloud management system; cloud resource auto-scaling system; cloud resource scaling; cloud service interface; cloud service provider; compute domain; enterprise on-demand; network domain; storage domain; thresholding mechanism; Computer architecture; Conferences; Correlation; Fires; Load modeling; Measurement; Monitoring; Cloud resource scaling; autonomic scaling; cloud computing; integrated compute; performance metrics; storage and network domain scaling; virtualized resources;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Network Operations and Management Symposium (NOMS), 2012 IEEE
  • Conference_Location
    Maui, HI
  • ISSN
    1542-1201
  • Print_ISBN
    978-1-4673-0267-8
  • Electronic_ISBN
    1542-1201
  • Type

    conf

  • DOI
    10.1109/NOMS.2012.6212070
  • Filename
    6212070