Cloud Infrastructure Provenance Collection and Management to Reproduce Scientific Workflow Execution

TL;DR

This paper introduces ReCAP, a framework for capturing Cloud infrastructure provenance to enable reproducibility of scientific workflows by re-provisioning similar resources on the Cloud.

Contribution

The paper proposes a novel framework and mapping approaches for capturing Cloud-aware provenance, facilitating workflow reproducibility and resource re-provisioning without performance overhead.

Findings

Provenance collection impacts workflow performance analysis.

Mapping approaches effectively capture Cloud information across scenarios.

ReCAP enables accurate re-provisioning of resources for reproducibility.

Abstract

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.