We propose a new heuristic called Resubmission Impact to support fault tolerant execution of scientific workflows in heterogeneous parallel and distributed computing environments. In contrast to related approaches, our method can be effectively used on new or unfamiliar environments, even in the absence of historical executions or failure trace models. On top of this method, we propose a dynamic enactment and rescheduling heuristic able to execute workflows with a high degree of fault tolerance, while taking into account soft deadlines. Simulated experiments of three real-world workflows in the Austrian Grid demonstrate that our method significantly reduces the resource waste compared to conservative task replication and resubmission techniques, while having a comparable makespan and only a slight decrease in the success probability. On the other hand, the dynamic enactment method manages to successfully meet soft deadlines in faulty environments in the absence of historical failure trace information or models.

Even though highly distributed environments such as Clouds and Grids are increasingly used for e-science high performance applications, they still cannot deliver the robustness and reliability needed for widespread acceptance as ubiquitous scientific tools. To overcome this problem, existing systems resort to fault tolerance mechanisms such as task replication and task resubmission. In this paper we propose a new heuristic called resubmission impact to enhance the fault tolerance support for scientific workflows in highly distributed systems. In contrast to related approaches, our method can be used effectively on systems even in the absence of historic failure trace data. Simulated experiments of three real scientific workflows in the Austrian Grid environment show that our algorithm drastically reduces the resource waste compared to conservative task replication and resubmission techniques, while having a comparable execution performance and only a slight decrease in the success probability.