To facilitate evaluation of workflow algorithms and systems on a range of workflow sizes, we have developed a set of synthetic workflow generator. This generator uses generators. These generators use 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 realistic, synthetic workflows resembling those used by real world scientific applications. .
The code used to generate all of the workflows below, and many others, is available from the GitHub repository. The java workflow generator sometimes generates negative task runtimes, so watch out for that.
These workflows come from a paper by Bharathi, et al. . There is another paper with more information about the workflows by Juve, et al. .
The code used to generate these workflows is available here. The code generator sometimes generates negative task runtimes, so watch out for that.
A large collection of DAXes similar to the ones listed below is available here. Note that it is about 375 MB.
|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)