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Optimizing utility in cloud computing through autonomic workload execution

Paton, N., de Aragão, M.A.T., Lee, K., Fernandes, A.A.A. and Sakellariou, R. (2009) Optimizing utility in cloud computing through autonomic workload execution. Bulletin of the Technical Committee on Data Engineering, 32 (1). pp. 51-58.

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    Abstract

    Cloud computing provides services to potentially numerous remote users with diverse requirements. Although predictable performance can be obtained through the provision of carefully delimited services, it is straightforward to identify applications in which a cloud might usefully host services that support the composition of more primitive analysis services or the evaluation of complex data analysis requests. In such settings, a service provider must manage complex and unpredictable workloads. This paper describes how utility functions can be used to make explicit the desirability of different workload evaluation strategies, and how optimization can be used to select between such alternatives. The approach is illustrated for workloads consisting of workflows or queries.

    Publication Type: Journal Article
    Publisher: IEEE
    Copyright: © 2009 IEEE
    URI: http://researchrepository.murdoch.edu.au/id/eprint/9965
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