Risk-aware checkpoint selection in cloud-based scientific workflow

Scientific workflows are generally computing- and data-intensive with large volume of data generated during their execution. Therefore, some of the data should be saved to avoid the expensive re-execution of tasks in case of exceptions. However, cloud-based data storage services come at some expense. In this paper, we extend the risk evaluation model, which assigns different weights to tasks based on their ordering relationship, to decide the occasion to perform backup or checkpoint service after the completion of a task. The proposed method computes and compares the potential loss with and without data backup to achieve the tradeoff between overhead of check pointing and re-execution after exceptions. We also design the utility function with the model and apply a genetic algorithm to find the optimized schedule. The results show that the robustness of the schedule is increased while the possible risk of failure is minimized, especially when the generated data is not large.