Services + Components = Data Intensive Scientific Workflow Applications with MeDICi
CBSE '09 Proceedings of the 12th International Symposium on Component-Based Software Engineering
Utility Driven Adaptive Work?ow Execution
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Performance analysis of dynamic workflow scheduling in multicluster grids
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Comparison of resource platform selection approaches for scientific workflows
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
Future Generation Computer Systems
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Workflows are widely used in applications that require coordinated use of computational resources. Workflow definition languages typically abstract over some aspects of the way in which a workflow is to be executed, such as the level of parallelism to be used or the physical resources to be deployed. As a result, a workflow management system has responsibility for establishing how best to execute a workflow given the available resources. The Pegasus workflow management system compiles abstract workflows into concrete execution plans, and has been widely used in large-scale e-Science applications. This paper describes an extension to Pegasus whereby resource allocation decisions are revised during workflow evaluation, in the light of feedback on the performance of jobs at runtime. The contributions of this paper include: (i) a description of how adaptive processing has been retrofitted to an existing workflow management system; (ii) a scheduling algorithm that allocates resources based on runtime performance; and (iii) an experimental evaluation of the resulting infrastructure using grid middleware over clusters.