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    A Framework for User Feedback Based Cloud Service Monitoring

    Access Status
    Fulltext not available
    Authors
    Hussain, Omar
    Rehman, Zia
    Parvin, Sazia
    Hussain, Farookh
    Date
    2012
    Type
    Conference Paper
    
    Metadata
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    Citation
    ur Rehman, Zia and Hussain, Omar and Parvin, Sazia and Hussain, Farookh. 2012. A Framework for User Feedback Based Cloud Service Monitoring, in Sixth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), Jul 4-6 2012, pp. 257-262. Palermo, Italy: IEEE.
    Source Title
    The 6th International Conference on Complex, Intelligent and Software Intensive Systems
    Source Conference
    CISIS 2012
    DOI
    10.1109/CISIS.2012.157
    ISBN
    978-1-4673-1233-2
    URI
    http://hdl.handle.net/20.500.11937/34475
    Collection
    • Curtin Research Publications
    Abstract

    The increasing popularity of the cloud computing paradigm and the emerging concept of federated cloud computing have motivated research efforts towards intelligent cloud service selection aimed at developing techniques for enabling the cloud users to gain maximum benefit from cloud computing by selecting services which provide optimal performance at lowest possible cost. Given the intricate and heterogeneous nature of current clouds, the cloud service selection process is, in effect, a multi criteria optimization or decision-making problem. The possible criteria for this process are related to both functional and nonfunctional attributes of cloud services. In this context, the two major issues are: (1) choice of a criteria-set and (2) mechanisms for the assessment of cloud services against each criterion for thorough continuous cloud service monitoring. In this paper, we focus on the issue of cloud service monitoring wherein the existing monitoring and assessment mechanisms are entirely dependent on various benchmark tests which, however, are unable to accurately determine or reliably predict the performance of actual cloud applications under a real workload. We discuss the recent research aimed at achieving this objective and propose a novel user-feedback-based approach which can monitor cloud performance more reliably and accurately as compared with the existing mechanisms.

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