GridFlow: workflow management for grid computing

By Junwei Cao, S. Jarvis, S. Saini, G. Nudd. CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings., 2003

431 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems and Management

View Paper: https://www.semanticscholar.org/paper/c1b779100b5e60a4079472b34857522ccb864123

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Rajkumar Buyya
2. Jia Yu
3. Ewa Deelman
4. Selim G. Akl
5. Fangpeng Dong
6. Gaurang Mehta
7. Karan Vahi
8. Miron Livny

In Information Systems and Management

1. Rajkumar Buyya
2. Ewa Deelman
3. Jia Yu
4. Miron Livny
5. John Good
6. Gurmeet Singh
7. Selim G. Akl
8. Fangpeng Dong

Citations in the corpus

• Computational models and heuristic methods for Grid scheduling problems (2010, neutral): “Such applications can take advantage of the power of Grid computing; however, the characteristics of the Grid environment make the coordination of its execution very complex [12,13].”
• A Taxonomy of Workflow Management Systems for Grid Computing (2005, methodology): “le lower-level sub-workflow schedulers. The manager is responsible for controlling the workflow execution and assigning the sub-workflows to the low-level schedulers. For example, in GridFlow project [25], there is one workflow manager and multiple lower-level schedulers. The workflow manager schedu”
• A Particle Swarm Optimization-Based Heuristic for Scheduling Workflow Applications in Cloud Computing Environments (2010, methodology): “Other well-known projects on workflow systems include GridFlow [4], ICENI [6], GridAnt [1], Triana [18] and Kepler [11].”
• A taxonomy of scientific workflow systems for grid computing (2005, neutral): “[ 4 ] DAG Abstract User-directed -Graph-based -Language-based Hierarchical Local Static -Simulation- based Performance- driven Task Level -Alternate resource Peer-to-Peer”
• Pegasus: A framework for mapping complex scientific workflows onto distributed systems (2005, methodology): “GridFlow [31] has a two-tiered architecture with global Grid workflow management and local Grid sub workflow scheduling.”
• Scheduling Algorithms for Grid Computing: State of the Art and Open Problems (2006, methodology): “We can find such tools like Condor DAGMan [127], CoG [74], Pegasus [37], GridFlow [21] and ASKALON [110].”
• Pegasus: Mapping Scientific Workflows onto the Grid (2004, methodology): “GridFlow [ 8 ] has a two-tiered architecture with global Grid workflow management and local Grid sub workflow scheduling.”