New aspect of investigating fault sensitivity of scientific workflows

Scientific workflows have emerged as a key technology that assists scientists with the design, management, execution, sharing and reuse of in silico experiments. These experiments are mainly data and compute intensive so they require high computing infrastructures to execute them. Since the complexity of these computing infrastructures is high so fault tolerant behavior must be supported to ensure the successful termination of the workflows while keeping to soft or hard deadlines and maintain usage costs at an optimum level. In this work we introduce a new aspect of supporting fault tolerance behavior or even task scheduling methods in time critical applications, which is based primarily on the topology of the workflow model. We show simple examples to prove the theoretical significance of our proposal.