A provenance-based fault tolerance mechanism for scientific workflows

Abstract

Capturing provenance information in scientific workflows is not only useful for determining data-dependencies, but also for a wide range of queries including fault tolerance and usage statistics. As collaborative scientific workflow environments provide users with reusable shared workflows, collection and usage of provenance data in a generic way that could serve multiple data and computational models become vital. This paper presents a method for capturing data value- and control- dependencies for provenance information collection in the Kepler scientific workflow system. It also describes how the collected information based on these dependencies could be used for a fault tolerance framework in different models of computation.