Abstract
Data-intensive workflows stage large amounts of data in and out of compute resources. The data staging strategies employed during the execution of such workflows can have a significant impact on the time taken to complete the execution or on the overall cost of the execution. We describe the problem of minimizing the overall time taken for execution and present a heuristic based on ordering clean-up jobs in the workflow. Next, we develop genetic algorithm based approaches to solving the same problem and demonstrate that the results obtained with the heuristic are comparable to the best results obtained with the genetic algorithm based approaches. We also describe the problem of minimizing the overall cost of execution and extend our genetic algorithm to generate schedules that vary the number of processors and the amount of storage provisioned for execution to generate low cost schedules.
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