GGF 10 Workflow Workshop Summary March 9 2004

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GGF 10 Workflow Workshop Summary March 9 2004 Berlin The Organizers

GGF 10 Workflow Workshop Summary March 9 2004 Berlin The Organizers

Topics • • • General Issues Application Requirements Language/User Interface Execution Engine (Run-time) 3

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

General Issues • Grain Size for “science” and efficiency of distributed service model •

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?

Application Requirements • • Time of running (seconds to months) People in loop Interactivity:

Application Requirements • • Time of running (seconds to months) People in loop Interactivity: real-time v batch Number of entities (10's to 100000'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

Language/User Interface • Abstract versus High level (specification) versus low-level (“workflow virtual machine” )

Language/User Interface • Abstract versus High level (specification) versus low-level (“workflow virtual machine” ) • Virtual Data • Abstraction level • Language: Kepler, Triana. BPEL 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?

Execution Engine (Run-time) • • • Performance Robustness Support Streams and Messages Discovery of

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

3 Grid Workflow Issues • 1) Analyze issues such as dataflow, scheduling, virtual data,

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”