Introduction to algorithms
Using random task graphs to investigate the potential benefits of heterogeneity in parallel systems
Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Benchmarking and comparison of the task graph scheduling algorithms
Journal of Parallel and Distributed Computing
DSC: Scheduling Parallel Tasks on an Unbounded Number of Processors
IEEE Transactions on Parallel and Distributed Systems
Sun Grid Engine: Towards Creating a Compute Power Grid
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
Realistic Modeling and Svnthesis of Resources for Computational Grids
Proceedings of the 2004 ACM/IEEE conference on Supercomputing
A performance study of job management systems: Research Articles
Concurrency and Computation: Practice & Experience - Systems Performance Evaluation
A taxonomy of scientific workflow systems for grid computing
ACM SIGMOD Record
GRENCHMARK: A Framework for Analyzing, Testing, and Comparing Grids
CCGRID '06 Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid
A large-scale study of failures in high-performance computing systems
DSN '06 Proceedings of the International Conference on Dependable Systems and Networks
Taverna: lessons in creating a workflow environment for the life sciences: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
Scientific workflow management and the Kepler system: Research Articles
Concurrency and Computation: Practice & Experience - Workflow in Grid Systems
How to measure a large open-source distributed system: Research Articles
Concurrency and Computation: Practice & Experience
Performance metrics and ontologies for Grid workflows
Future Generation Computer Systems
Build-and-Test Workloads for Grid Middleware: Problem, Analysis, and Applications
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Overhead Analysis of Scientific Workflows in Grid Environments
IEEE Transactions on Parallel and Distributed Systems
Future Generation Computer Systems
The performance of bags-of-tasks in large-scale distributed systems
HPDC '08 Proceedings of the 17th international symposium on High performance distributed computing
On the dynamic resource availability in grids
GRID '07 Proceedings of the 8th IEEE/ACM International Conference on Grid Computing
How are Real Grids Used? The Analysis of Four Grid Traces and Its Implications
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
Measuring the Performance and Reliability of Production Computational Grids
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
Modeling job arrivals in a data-intensive grid
JSSPP'06 Proceedings of the 12th international conference on Job scheduling strategies for parallel processing
POGGI: Puzzle-Based Online Games on Grid Infrastructures
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Performance analysis of dynamic workflow scheduling in multicluster grids
Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing
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
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To benefit from grids, scientists require grid workflow engines that automatically manage the execution of inter-related jobs on the grid infrastructure. So far, the workflows community has focused on scheduling algorithms and on interface tools. Thus, while several grid workflow engines have been deployed, little is known about their performance-related characteristics, and there are no commonly-used testing practices. This situation limits the adoption of the grid workflow engines, and hampers their tuning and their further development. In this work we propose a testing methodology for grid workflow engines that focuses on five characteristics: overhead, raw performance, stability, scalability, and reliability. Using this methodology, we evaluate in a real test environment several middleware stacks that include grid workflow engines, including two based on DAGMan/Condor and on Karajan/Globus.