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Reproducibility of scientific workflows execution using cloud-aware provenance (ReCAP)

Hasham, Khawar; Munir, Kamran


Khawar Hasham

Kamran Munir
Associate Professor in Data Science


© 2018, Springer-Verlag GmbH Austria, part of Springer Nature. Provenance of scientific workflows has been considered a mean to provide workflow reproducibility. However, the provenance approaches adopted so far are not applicable in the context of Cloud because the provenance trace lacks the Cloud information. This paper presents a novel approach that collects the Cloud-aware provenance and represents it as a graph. The workflow execution reproducibility on the Cloud is determined by comparing the workflow provenance at three levels i.e., workflow structure, execution infrastructure and workflow outputs. The experimental evaluation shows that the implemented approach can detect changes in the provenance traces and the outputs produced by the workflow.

Journal Article Type Article
Publication Date Dec 1, 2018
Journal Computing
Print ISSN 0010-485X
Publisher Springer (part of Springer Nature)
Peer Reviewed Peer Reviewed
Volume 100
Issue 12
Pages 1299-1333
APA6 Citation Munir, K. (2018). Reproducibility of scientific workflows execution using cloud-aware provenance (ReCAP). Computing, 100(12), 1299-1333.
Keywords scientific workflows, cloud computing, provenance management, provenance graph, reproducibility
Publisher URL
Additional Information Additional Information : The final publication is available at Springer via


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