Optimizing Grid-Based Workflow Execution

Abstract Computational Grids provide the resources for executing large scale application that are not possible to execute on a single resources. Workflows are being used as the programming paradigm for the applications that need Grid resources. A workflow is represented as a DAG where the vertices are the compute tasks and the edges are the data dependencies between the tasks. Previous research has focussed on developing algorithms for scheduling them on Grid resources and easy to use interfaces for composing workflows and submitting them on the Grid. In this research, we focus on the behavior of the workflow execution engine using a dynamic Condor pool as an instantiation of a Distributed Virtual Computer. We show that the efficiency of the workflow execution engine in executing the workflow can make a large impact on the workflow completion times for fine granularity workflows. We examine the factors that affect the performance of the workflow execution engine and their impact on the completion time. Finally we explore some restructuring techniques that can further improve the workflow completion time. Poster Contributors Gurmeet Singh, Carl Kesselman and Ewa Deelman Poster Presented by Gurmeet Singh

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