A taxonomy of scientific workflow systems for grid computing
ACM SIGMOD Record
Web services navigator: visualizing the execution of web services
IBM Systems Journal
Performance metrics and ontology for describing performance data of grid workflows
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid - Volume 01
SCALEA-G: A unified monitoring and performance analysis system for the grid
Scientific Programming - AxGrids 2004
ASKALON: A Grid Application Development and Computing Environment
GRID '05 Proceedings of the 6th IEEE/ACM International Workshop on Grid Computing
Performance metrics and ontologies for Grid workflows
Future Generation Computer Systems
Proceedings of the 2008 ACM/IEEE conference on Supercomputing
DIPAS: A distributed performance analysis service for grid service-based workflows
Future Generation Computer Systems
Failure prediction and localization in large scientific workflows
Proceedings of the 6th workshop on Workflows in support of large-scale science
Online workflow management and performance analysis with stampede
Proceedings of the 7th International Conference on Network and Services Management
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The execution of scientific workflows in Grids can imply complex interactions among various Grid applications and resources spanning multiple organizations. Failures and performance problems can easily occur during the execution. However, online monitoring and detecting failures and performance problems of scientific workflows in Grids is a nontrivial task. So far little effort has been spent on supporting performance monitoring and visualization of scientific workflows for the Grid. In this paper we present an online workflow performance monitoring and visualization tool for Grid scientific workflows that is capable to monitor the performance and to detect failures of Grid workflows. We also support sophisticated visualization of monitoring and performance result. Performance monitoring is conducted online and Grid infrastructure monitoring is integrated with workflow monitoring, thus fostering the chance to detect performance problems and being able to correlate performance metrics from different sources.