Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
SIAM Journal on Discrete Mathematics
Applying Chimera virtual data concepts to cluster finding in the Sloan Sky Survey
Proceedings of the 2002 ACM/IEEE conference on Supercomputing
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
GriPhyN and LIGO, Building a Virtual Data Grid for Gravitational Wave Scientists
HPDC '02 Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing
The Cactus Worm: Experiments with Dynamic Resource Discovery and Allocation in a Grid Environment
International Journal of High Performance Computing Applications
Toward a Theory for Scheduling Dags in Internet-Based Computing
IEEE Transactions on Computers
Workflows for e-Science: Scientific Workflows for Grids
Workflows for e-Science: Scientific Workflows for Grids
Pegasus: A framework for mapping complex scientific workflows onto distributed systems
Scientific Programming
A low-cost rescheduling policy for efficient mapping of workflows on grid systems
Scientific Programming - AxGrids 2004
Self-adjustment of resource allocation for grid applications
Computer Networks: The International Journal of Computer and Telecommunications Networking
A performance-oriented adaptive scheduler for dependent tasks on grids
Concurrency and Computation: Practice & Experience - Middleware for Grid Computing: Future Trends (MGC2006)
Communications of the ACM
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Grids and clouds are utilized for the execution of applications composed of dependent tasks, usually modeled as workflows. To efficiently run the application, a scheduler must distribute the components of the workflow in the available resources using information about duration of tasks and communication between tasks in the workflow. However, such information may be subject to imprecisions, thus not reflecting what is observed during the execution. In this paper we propose a simple way of representing the costs of the components in a workflow in order to reduce the impact of uncertainties introduced by wrong estimations, and also to ease the application specification for the user. Evaluation shows that the use of relative costs in tasks and dependencies can improve in many cases the resulting schedule when compared to cases where the input data carries an uncertainty of 20% and 50%.