A Probabilistic Strategy for Setting Temporal Constraints in Scientific Workflows
BPM '08 Proceedings of the 6th International Conference on Business Process Management
Using Templates to Predict Execution Time of Scientific Workflow Applications in the Grid
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
A Hybrid Intelligent Method for Performance Modeling and Prediction of Workflow Activities in Grids
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
A performance study of grid workflow engines
GRID '08 Proceedings of the 2008 9th IEEE/ACM International Conference on Grid Computing
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Localising temporal constraints in scientific workflows
Journal of Computer and System Sciences
Journal of Systems and Software
A data placement strategy in scientific cloud workflows
Future Generation Computer Systems
Negotiation-Based Scheduling of Scientific Grid Workflows Through Advance Reservations
Journal of Grid Computing
Journal of Systems and Software
ACM Transactions on Software Engineering and Methodology (TOSEM)
Metrics for heterogeneous scientific workflows: A case study of an earthquake science application
International Journal of High Performance Computing Applications
Workflow overhead analysis and optimizations
Proceedings of the 6th workshop on Workflows in support of large-scale science
DAGwoman: enabling DAGMan-like workflows on non-Condor platforms
Proceedings of the 1st ACM SIGMOD Workshop on Scalable Workflow Execution Engines and Technologies
Do we need to handle every temporal violation in scientific workflow systems?
ACM Transactions on Software Engineering and Methodology (TOSEM)
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Scientific workflows are a topic of great interest in the Grid community that sees in the workflow model an attractive paradigm for programming distributed wide-area Grid infrastructures. Traditionally, the Grid workflow execution is approached as a pure best-effort scheduling problem that maps the activities onto the Grid processors based on appropriate optimisation or local matchmaking heuristics such that the overall execution time is minimised. Even though such heuristics often deliver effective results, the execution in dynamic and unpredictable Grid environments is prone to severe performance losses that must be understood for minimising the completion time or for efficient use of high- erformance resources. In this paper, we propose a new systematic approach to help the scientists and middleware developers understand the most severe sources of performance losses that occur when executing scientific workflows in dynamic Grid environments. We introduce an ideal model for the lowest execution time that can be achieved by a workflow and explain the difference to the real measured Grid execution time based on a hierarchy of performance overheads for Grid computing. We describe how to systematically measure and compute the overheads from individual activities to larger workflow regions and adjust well-known parallel processing metrics to the scope of Grid computing, including speedup and efficiency. We present a distributed online tool for computing and analysing the performance overheads in realtime based on event correlation techniques and introduce several performance contracts, as quality of service parameters to be enforced during the workflow execution beyond traditional best-effort practices. We illustrate our method through post-mortem and online performance analysis of two real-world workflow applications executed in the Austrian Grid environment.