To facilitate evaluation of workflow algorithms and systems on a range of workflow sizes, we have developed a workflow generator. This generator uses the information gathered from actual executions of scientific workflows on the Grid as well as our understanding of the processes behind these workflows to generate synthetic workflows resembling those used by real world scientific applications.
These workflows come from a paper by Bharathi, et al. . There is another paper with more information about the workflows by Juve, et al. .
|Workflow Type||Figure in Report||Example||DAX|
|LEAD Mesoscale Meteorology||Figure 1||leadmm.xml|
|LEAD ARPS Data Analysis System||Figure 2|
|LEAD Data Mining Workflow||Figure 3||leaddm.xml|
|Storm Surge SCOOP Workflow||Figure 4|
|Floodplain Mapping||Figure 5||floodplain.xml|
|Motif Network||Figure 8|
|Molecular Sciences||Figure 10||molsci.xml|
|Avian Flu||Figure 11|
|Pan-STARRS Load||Figure 13|
|Pan-STARRS Merge||Figure 14|
 R. F. da Silva, W. Chen, G. Juve, K. Vahi, E. Deelman. Community Resources for Enabling Research in Distributed Scientific Workflows. 10th IEEE International Conference on e-Science (eScience 2014)
 S. Bharathi, A. Chervenak, E. Deelman, G. Mehta, M.-H. Su, and K. Vahi, “Characterization of Scientific Workflows”, 3rd Workshop on Workflows in Support of Large Scale Science (WORKS 08), 2008.
 , " Characterizing and Profiling Scientific Workflows", Future Generation Computer Systems , 29:3, pp. 682–692, March 2013 .
 L. Ramakrishnan and D. Gannon, "A Survey of Distributed Workflow Characteristics and Resource Requirements", Indiana University Technical Report TR671, 2008.