  The emergence of Cloud computing provides a new computing paradigm for
scientific workflow execution. It provides dynamic, on-demand and scalable
resources that enable the processing of complex workflow-based experiments.
With the ever growing size of the experimental data and increasingly complex
processing workflows, the need for reproducibility has also become essential.
Provenance has been thought of a mechanism to verify a workflow and to provide
workflow reproducibility. One of the obstacles in reproducing an experiment
execution is the lack of information about the execution infrastructure in the
collected provenance. This information becomes critical in the context of Cloud
in which resources are provisioned on-demand and by specifying resource
configurations. Therefore, a mechanism is required that enables capturing of
infrastructure information along with the provenance of workflows executing on
the Cloud to facilitate the re-creation of execution environment on the Cloud.
This paper presents a framework, ReCAP, along with the proposed mapping
approaches that aid in capturing the Cloud-aware provenance information and
help in re-provisioning the execution resource on the Cloud with similar
configurations. Experimental evaluation has shown the impact of different
resource configurations on the workflow execution performance, therefore
justifies the need for collecting such provenance information in the context of
Cloud. The evaluation has also demonstrated that the proposed mapping
approaches can capture Cloud information in various Cloud usage scenarios
without causing performance overhead and can also enable the re-provisioning of
resources on Cloud. Experiments were conducted using workflows from different
scientific domains such as astronomy and neuroscience to demonstrate the
applicability of this research for different workflows.
