Reasoning task dependencies for robust service selection in data intensive workflows

Selecting appropriate services for task execution in workflows should not only consider budget and deadline constraints, but also ensure the best probability that workflow will succeed and minimize the potential loss in case of exceptions. This requirement is more critical for data-intensive applications in grids or clouds since any failure is costly. Therefore, we design a fine-grained risk evaluation model customized for workflows to precisely compute the cost of failure for selected services. 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 service allocations, thereby maximizing the robustness of the workflow while minimizing the possible risk of failure. Experiments and analysis show that the application of customized risk evaluation model into service selection can generally improve the successful probability of a workflow while reducing its exposure to the risk.