This page is no longer maintained. We have released a new Workflow Generator as part of the WorkflowHub Project:
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Workflow Generator
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.
Additional details about this work can be found in our publication at WORKS08
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The code used to generate all of the synthetic 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.
Simulator
WorkflowSim can be used to simulate the workflows generated by the Workflow Generator.
Traces
Traces and execution logs from real workflows are available here: here, here, and here. Data sets like these were used to parameterize the Workflow Generator.
Synthetic Workflows
Pegasus Workflows
These workflows come from a paper by Bharathi, et al. [2]. There is another paper with more information about the workflows by Juve, et al. [3].
A large collection of DAXes similar to the ones listed below is available here. Note that it is about 375 MB.
Workflow Type | Example | DAX |
|---|---|---|
Montage |  | |
CyberShake |  | |
Epigenomics |  | |
LIGO Inspiral Analysis |  | |
SIPHT |  |
Ramakrishnan and Gannon Workflows
These workflows come from a report by Ramakrishnan and Gannon [3].
| Workflow Type | Figure in Report | Example | DAX |
|---|
A large collection of DAXes similar to the ones listed above is available here. Note that is is about 290MB.
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| 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 |
| Glimmer | Figure 6 |  | glimmer.xml |
| Gene2Life | Figure 7 |  | gene2life.xml |
| Motif Network | Figure 8 |  | |
| MEME-MAST | Figure 9 |  | mememast.xml |
| Molecular Sciences | Figure 10 |  | molsci.xml |
| Avian Flu | Figure 11 |  | |
| caDSR | Figure 12 |  | cadsr.xml |
| Pan-STARRS Load | Figure 13 |  | |
| Pan-STARRS Merge | Figure 14 |  | |
| McStas | Figure 15 |  | mcstas.xml |
[1] 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)
[2] 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.
[3] , "Characterizing and Profiling Scientific Workflows", Future Generation Computer Systems , 29:3, pp. 682–692, March 2013 .
[4] L. Ramakrishnan and D. Gannon, "A Survey of Distributed Workflow Characteristics and Resource Requirements", Indiana University Technical Report TR671, 2008.