This paper investigates Data-Preservation, a feature of Scientific Workflow Middleware (SWM) useful for supporting data provenance and "smart recomputation." We observe that in order for an SWM supporting Data Preservation to achieve decent performance, it should execute on top of copy-on-write file systems. Unfortunately, most file systems in-use at scientific computing facilities were designed without copy-on-write semantics. In response, we design, implement and evaluate a middleware-level solution that is based on user-provided hints and parallelization. The solution can be deployed on top of current file systems and is able to scale almost arbitrarily. Our validation is based on real use-cases from astrophysics and experiments on a cluster with 4 file systems.