Condor-G: A Computation Management Agent for Multi-Institutional Grids
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The Philosophy of TeraGrid: Building an Open, Extensible, Distributed TeraScale Facility
CCGRID '02 Proceedings of the 2nd IEEE/ACM International Symposium on Cluster Computing and the Grid
Workflow management in GriPhyN
Grid resource management
E-SCIENCE '06 Proceedings of the Second IEEE International Conference on e-Science and Grid Computing
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Overhead Analysis of Scientific Workflows in Grid Environments
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Grid Computing: Achievements and Prospects
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GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
An integrated framework for performance-based optimization of scientific workflows
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Experiences with resource provisioning for scientific workflows using Corral
Scientific Programming
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DAGwoman: enabling DAGMan-like workflows on non-Condor platforms
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Oozie: towards a scalable workflow management system for Hadoop
Proceedings of the 1st ACM SIGMOD Workshop on Scalable Workflow Execution Engines and Technologies
Imbalance optimization in scientific workflows
Proceedings of the 27th international ACM conference on International conference on supercomputing
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The execution of scientific workflows often suffers from a variety of overheads in distributed environments. It is essential to identify the different overheads and to evaluate how optimization methods help reduce overheads and improve runtime performance. In this paper, we present an overhead analysis for a set of workflow runs on cloud and grid platforms. We present the overhead distributions and conclude that they satisfy an exponential or uniform distribution. We compare three methods to calculate the cumulative sum of the overheads based on how they overlap. In addition, we indicate how experimental parameters impact the overhead and thereby the overall workflow performance. We then show how popular optimization methods improve runtime performance by reducing some or all types of overheads.