Scientific Workflow Repeatability through Cloud-Aware Provenance
Khawar Hasham, Kamran Munir, Jetendr Shamdasani, Richard McClatchey

TL;DR
This paper introduces a novel method for ensuring scientific workflow repeatability in Cloud environments by capturing and utilizing Cloud infrastructure provenance to re-provision resources and re-execute workflows.
Contribution
It proposes a new approach to collect Cloud infrastructure provenance and map it to workflow provenance for improved repeatability in Cloud-based scientific workflows.
Findings
Feasibility demonstrated through initial prototype evaluation
Effective collection of Cloud infrastructure and workflow provenance
Potential for enhanced workflow repeatability in Cloud environments
Abstract
The transformations, analyses and interpretations of data in scientific workflows are vital for the repeatability and reliability of scientific workflows. 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. The dynamic nature of the Cloud in comparison to the Grid makes it difficult because resources are provisioned on-demand unlike the Grid. This paper presents a novel approach that can assist in mitigating this challenge. This approach can collect Cloud infrastructure information along with workflow provenance and can establish a mapping…
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