Kepler: An Extensible System for Design and Execution of Scientific Workflows
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Provenance for Computational Tasks: A Survey
Computing in Science and Engineering
A Provenance-Based Fault Tolerance Mechanism for Scientific Workflows
Provenance and Annotation of Data and Processes
A break in the clouds: towards a cloud definition
ACM SIGCOMM Computer Communication Review
Towards Flexible Event-Handling in Workflows through Data States
SERVICES '10 Proceedings of the 2010 6th World Congress on Services
SciPhy: a cloud-based workflow for phylogenetic analysis of drug targets in protozoan genomes
BSB'11 Proceedings of the 6th Brazilian conference on Advances in bioinformatics and computational biology
An adaptive parallel execution strategy for cloud-based scientific workflows
Concurrency and Computation: Practice & Experience
Capturing and querying workflow runtime provenance with PROV: a practical approach
Proceedings of the Joint EDBT/ICDT 2013 Workshops
Provenance traces from Chiron parallel workflow engine
Proceedings of the Joint EDBT/ICDT 2013 Workshops
User-steering of HPC workflows: state-of-the-art and future directions
Proceedings of the 2nd ACM SIGMOD Workshop on Scalable Workflow Execution Engines and Technologies
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Capturing provenance data in scientific workflows is a key issue since it allows for reproducibility and evaluation of results. Many of these workflows generate around 100,000 tasks that execute in parallel in High Performance Computing environments, such as large clusters and clouds. SciCumulus is a workflow engine for parallel execution in clouds. Activity failure is almost inevitable in clouds where virtual machine failures are a reality rather than a possibility. We present SciMultaneous, a service architecture that manages re-executions of failed scientific workflow tasks using runtime provenance. Experimental results on clouds showed that SciMultaneous considerably increases the workflow completion and reduces the total execution time of the workflow (considering executions and re-executions) up to 11.5%, when compared to ad-hoc approaches.