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Pegasus: Planning for Execution in Grids http://pegasus.isi.edu. Virtual Data Concepts. -- Capture and manage information about relationships among -- Data (of widely varying representations) -- Programs (& their execution needs) -- Computations (& execution environments)
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Pegasus: Planning for Execution in Grids http://pegasus.isi.edu Virtual Data Concepts -- Capture and manage information about relationships among -- Data (of widely varying representations) -- Programs (& their execution needs) -- Computations (& execution environments) -- Apply this information to, e.g. -- Discovery: Data and program discovery -- Workflow: Structured paradigm for organizing, locating, specifying, & requesting data -- Explanation: provenance -- Ressearch part of NSF funded GriPhyN project Logic Pegasus: Planning for Execution in Grids -- Maps from abstract to concrete workflow -- Algorithmic and AI based techniques -- Automatically locates physical locations for both components (transformations) and data -- Uses Globus RLS and the Transformation Catalog -- Finds appropriate resources to execute -- via Globus MDS -- Reuses existing data products where applicable -- Publishes newly derived data products -- Chimera virtual data catalog & MCS -- Uses Globus COG Kit for authentication Planning and Scheduling Granularity Deferred Planning -- Partitioning -- Allows to set the granularity for planning ahead. -- Node Aggregation -- Allows to combine nodes in the workflow and schedule them as one unit. -- Minimizes scheduling overhead and planning overhead -- Related But Separate Concepts -- Small Jobs > High level of Node Aggregation > Large Partitions -- Very Dynamic System > Small Partitions Re Planning -- Leverage Condor’s job retry mechanism to trigger retry on partition in case of failure. -- Parse Condor Log files to determine the sites at which job failed. -- Subsequent invocation of Pegasus on the same partition are aware of the bad sites. Pegasus Portal Grid Setup People Involved: USC/ISI:Ewa Deelman, Carl Kesselman, Gaurang Mehta, Gurmeet Singh, Mei-Hui Su, Karan Vahi ,James Blythe, Yolanda Gil