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The execution of data-intensive workflow applications in scientific and enterprise grids has gained popularity in recent times. Such applications process large and dynamic data sets, and often present scope for optimized data handling that can be exploited for performance. Traditionally, core grid middleware technologies of scheduling and orchestration, have treated data management as a background...
In this paper, it describes the QoS-GRAF, a framework for providing revenue maximization in a utility computing grid where jobs have multiple resource dependencies and differentiated QoS pricing. To solve the revenue maximization problem the linear relaxation based algorithms, MRPA and MLBA, that achieve performance within 1-5% of the optimal solution and significantly outperform alternative approaches...
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