  Provenance has been thought of a mechanism to verify a workflow and to
provide workflow reproducibility. This provenance of scientific workflows has
been effectively carried out in Grid based scientific workflow systems.
However, recent adoption of Cloud-based scientific workflows present an
opportunity to investigate the suitability of existing approaches or propose
new approaches to collect provenance information from the Cloud and to utilize
it for workflow repeatability in the Cloud infrastructure. This paper presents
a novel approach that can assist in mitigating this challenge. This approach
can collect Cloud infrastructure information from an outside Cloud client along
with workflow provenance and can establish a mapping between them. This mapping
is later used to re-provision resources on the Cloud for workflow execution.
The reproducibility of the workflow execution is performed by: (a) capturing
the Cloud infrastructure information (virtual machine configuration) along with
the workflow provenance, (b) re-provisioning the similar resources on the Cloud
and re-executing the workflow on them and (c) by comparing the outputs of
workflows. The evaluation of the prototype suggests that the proposed approach
is feasible and can be investigated further. Moreover, there is no reference
reproducibility model exists in literature that can provide guidelines to
achieve this goal in Cloud. This paper also attempts to present a model that is
used in the proposed design to achieve workflow reproducibility in the Cloud
environment.
