• DocumentCode
    2997144
  • Title

    A Service Level Agreement for the Resource Transaction Risk Based on Cloud Bank Model

  • Author

    Mojun Su ; Hao Li ; ShengLin Yang ; Lu, Jun

  • Author_Institution
    Sch. of Inf. Sci. & Eng., Yunnan Univ., Kunming, China
  • fYear
    2012
  • fDate
    22-24 Nov. 2012
  • Firstpage
    198
  • Lastpage
    203
  • Abstract
    Cloud computing is a new business computing model. The environment of the resources is very complex. The resources are physically distributed and connected by the network. There are many risks existing in the resource transactions. So how to make sure that the cloud computing platform can avoid these risks during the transactions and assure the quality of services (QoS) provided to the consumers is a very important issue in cloud computing. Service Level Agreement (SLA) is proposed to solve the problems between the customers and service suppliers. Cloud Bank model [1] is a resource management model based on economic principles and aims at solving all the commercial level problems in cloud computing. This paper presents a framework of Service Level Agreement based on the Cloud Bank´s liquidity risk [2] predicting model. This SLA can help the Cloud Bank avoid the risks and assure the QoS to the consumers.
  • Keywords
    cloud computing; contracts; resource allocation; risk management; transaction processing; QoS; SLA; business computing model; cloud bank liquidity risk predicting model; cloud computing; commercial level problems; economic principles; quality of services; resource management model; resource transaction risk; service-level agreement; Cloud computing; Computational modeling; Contracts; Educational institutions; Monitoring; Predictive models; Quality of service; Cloud Bank Model; Cloud Computing; Liquidity Risk; Risk Prediction; Service Level Agreement;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Cloud and Service Computing (CSC), 2012 International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4673-4724-2
  • Type

    conf

  • DOI
    10.1109/CSC.2012.38
  • Filename
    6414500