DescriptionNext-generation data-intensive scientific workflows need to support streaming and real-time applications with dynamic resource needs on high performance computing (HPC) platforms. The static resource allocation model on current HPC systems that was designed for monolithic MPI applications is insufficient to support the elastic resource needs of current and future workflows. In this paper, we discuss the design, implementation and evaluation of Elastic-HPC (E-HPC), an elastic framework for managing resources for scientific workflows on current HPC systems. E-HPC considers a resource slot for a workflow as an elastic window that might map to different physical resources over the duration of a workflow. Our framework uses checkpoint-restart as the underlying mechanism to migrate workflow execution across the dynamic window of resources. E-HPC provides the foundation necessary to enable dynamic resource allocation of HPC resources that are needed for streaming and real-time workflows. E-HPC has negligible overhead beyond the cost of checkpointing. Additionally, E-HPC results in decreased turnaround time of workflows compared to traditional model of resource allocation for workflows, where resources are allocated per stage of the workflow. Our evaluation shows that E-HPC improves core hour utilization for common workflow resource use patterns and provides an effective framework for elastic expansion of resources for applications with dynamic resource needs.