Exception handling patterns for hierarchical scientific workflows

Scientific workflows generally involve the distribution of tasks to distributed resources, which may exist in different administrative domains. Such a distribution may lead to faults that may arise at different levels: application level, enactment level, and resource management level, for instance. Detecting these faults, and subsequently adapting the structure of the workflow dynamically remains an important challenge. An approach to supporting such dynamic adaptation is presented, along with an evaluation of the approach using an example from the myexperiment.org workflow repository. An analysis of the overhead in using the approach is also presented, along with the benefits/pitfalls of using the proposed approach.