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Karma2: Provenance Management for Data-Driven Workflows

Karma2: Provenance Management for Data-Driven Workflows

Yogesh L. Simmhan, Beth Plale, Dennis Gannon
Copyright: © 2009 |Pages: 24
ISBN13: 9781605663708|ISBN10: 1605663700|ISBN13 Softcover: 9781616924683|EISBN13: 9781605663715
DOI: 10.4018/978-1-60566-370-8.ch020
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MLA

Simmhan, Yogesh L., et al. "Karma2: Provenance Management for Data-Driven Workflows." Quantitative Quality of Service for Grid Computing: Applications for Heterogeneity, Large-Scale Distribution, and Dynamic Environments, edited by Lizhe Wang, et al., IGI Global, 2009, pp. 380-403. https://doi.org/10.4018/978-1-60566-370-8.ch020

APA

Simmhan, Y. L., Plale, B., & Gannon, D. (2009). Karma2: Provenance Management for Data-Driven Workflows. In L. Wang, J. Chen, & W. Jie (Eds.), Quantitative Quality of Service for Grid Computing: Applications for Heterogeneity, Large-Scale Distribution, and Dynamic Environments (pp. 380-403). IGI Global. https://doi.org/10.4018/978-1-60566-370-8.ch020

Chicago

Simmhan, Yogesh L., Beth Plale, and Dennis Gannon. "Karma2: Provenance Management for Data-Driven Workflows." In Quantitative Quality of Service for Grid Computing: Applications for Heterogeneity, Large-Scale Distribution, and Dynamic Environments, edited by Lizhe Wang, Jinjun Chen, and Wei Jie , 380-403. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-370-8.ch020

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Abstract

The increasing ability for the sciences to sense the world around us is resulting in a growing need for datadriven e-Science applications that are under the control of workflows composed of services on the Grid. The focus of our work is on provenance collection for these workflows that are necessary to validate the work-flow and to determine quality of generated data products. The challenge we address is to record uniform and usable provenance metadata that meets the domain needs while minimizing the modification burden on the service authors and the performance overhead on the workflow engine and the services. The framework is based on generating discrete provenance activities during the lifecycle of a workflow execution that can be aggregated to form complex data and process provenance graphs that can span across workflows. The implementation uses a loosely coupled publish-subscribe architecture for propagating these activities, and the capabilities of the system satisfy the needs of detailed provenance collection. A performance evaluation of a prototype finds a minimal performance overhead (in the range of 1% for an eight-service workflow using 271 data products).

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