1. GGF10 Workflow Workshop Summary
March Berlin
The Organizers

2. Topics General Issues Application Requirements Language/User Interface
Execution Engine (Run-time)
3 Grid Workflow Issues

3. General Issues
Grain Size for “science” and efficiency of distributed service model
Hierarchy (workflow of workflows)
Data versus Control
Security
Metadata and Provenance
Dynamic/Event based or Static
Component Models/Architecture -- CCA, OGSI, WSRF. Web Services
Error Handling (Detect, Specify action, Take action)
Ease of Use (for real users not Grid hackers)
Collaborative use by several users
Open Source?

4. Application Requirements
Time of running (seconds to months)
People in loop
Interactivity: real-time v batch
Number of entities (10's to 's)
Stream-based (communicate via pipes) OR
Job-based (communicate via files)
Spatial versus temporal interactions
Multiple “workflow job” instances handled in or outside workflow

5. Language/User Interface
Abstract versus High level (specification) versus low-level (“workflow virtual machine” )
Virtual Data
Abstraction level
Language: Kepler, Triana. BPEL WSCL WSCI …..
BPEL is inevitable?
Diversity via Different “towers” in BPEL
And/Or another language
Does “other language” map to BPEL as low level interoperable Workflow VM
Scripts: Perl, Python, Ant, Matlab, Specialized
Petri Nets
Functional Language specification
Graphical UI
Dataflow (stream) versus Control (message) model
Web Service ports can be data and control?

6. Execution Engine (Run-time)
Performance
Robustness
Support Streams and Messages
Discovery of Services and Resources (computers, data repositories, networks)
Support Scheduling/Planning of tasks and/or streams and/or data resources (“Towers”?)
Support of Monitoring, Factories, Life-times etc
Type checking
Support Debugging
Support "Workflow" (Computational) Steering
Distributed versus centralized implementation

7. 3 Grid Workflow Issues
1) Analyze issues such as dataflow, scheduling, virtual data, “science state”
Map into WSRF and BPEL Correlation Identifiers/Extensibility or find “inadequacies”?
2) Look at scale and data size, data locality issues in science workflow
What are implications for runtime engine?
3) Examine Semantic Grid (metadata/ provenance) issues for workflow
2) and 3) can be examined for both BPEL and “other approaches”