The robustness of a schedule, with respect to its probability of successful execution, becomes an indispensable requirement in open and dynamic service-oriented environment, such as grids or clouds. We design a fine-grained risk assessment model customized for workflows to precisely compute the cost of failure of a schedule. In comparison with current course-grained model, ours takes the relation of task dependency into consideration and assigns higher impact factor to tasks at the end. Thereafter, we design the utility function with the model and apply a genetic algorithm to find the optimized schedule, thereby maximizing the robustness of the schedule while minimizing the possible risk of failure. Experiments and analysis show that the application of customized risk assessment model into scheduling can generally improve the successful probability of a schedule while reducing its exposure to the risk.
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