In large scale scientific computing, proper planning and management of computational resources lead to higher system utilizations and increased scientific productivity. Scientists are increasingly leveraging the use of business process management techniques and workflow management tools to balance the needs of the scientific analyses with the availability of computational resources. However, the advancements in productivity from execution of workflows in a large scale computing environments are often thwarted by runtime resource failures. This paper presents our initial work toward autonomic model based fault analysis in workflow based environments