Risk-aware intermediate dataset backup strategy in cloud-based data intensive workflows

Abstract

Data-intensive 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 introduce the risk evaluation model tailored for workflow structure to measure and achieve the trade-off between the overhead of backup storage and the cost of data regeneration in failure, making the service selection and execution more efficient and robust. The proposed method computes and compares the potential loss with and without data backup to achieve the trade-off between overhead of intermediate dataset backup and task 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 volume of generated data is not large in comparison with the input.