Dynamic workflow in a grid enabled problem solving environment
In a Problem Solving Environment (PSE), a scientific workflow management system (SWMS) provides a meta environment for managing activities and data in scientific experiments, for prototyping experimental computing systems and for orchestrating the runtime system behaviour. A Grid infrastructure make...
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| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/96178 http://hdl.handle.net/10220/10155 http://www.computer.org/csdl/proceedings/cit/2005/2432/00/24320339-abs.html |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | In a Problem Solving Environment (PSE), a scientific workflow management system (SWMS) provides a meta environment for managing activities and data in scientific experiments, for prototyping experimental computing systems and for orchestrating the runtime system behaviour. A Grid infrastructure makes data and computing intensive experiments feasible in PSEs but also requires the management of workflow to support dynamics of the flow execution. A dynamic SWMS includes a human user in the runtime loop of a flow execution, and allows an engine to flexibly orchestrate a workflow according to the human decision and the runtime states of the environment. In this paper, we present our research in an ongoing project: Virtual Laboratory for e-Science (VL-e). An agent based solution is proposed to enhance an existing Grid enabled Problem Solving Environment framework called VLAM-G. The intelligence for problem solving strategies and for workflow orchestration is encapsulated as knowledge in two types of agents: study managers and scenario conductors. |
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