Trust-based robust scheduling and runtime adaptation of scientific workflow

Robustness and reliability with respect to the successful completion of a schedule are crucial requirements for scheduling in scientific workflow management systems because service providers are becoming autonomous. We introduce a model to incorporate trust which indicates the probability that a service agent will comply with its commitments to improve the predictability and stability of the schedule. To deal with exceptions during the execution of a schedule, we adapt and evolve the schedule at runtime by interleaving the processes of evaluating, scheduling, executing and monitoring in the life cycle of the workflow management. Experiments show that schedules maximizing participants’ trust are more likely to survive and succeed in open and dynamic environments. The results also prove that the proposed approach of workflow evaluation can find the most robust execution flow efficiently, thus avoiding the need of scheduling every possible execution path in the workflow definition.