Scheduling parallel program tasks onto arbitrary target machines
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
List scheduling of parallel tasks
Information Processing Letters
The grid: blueprint for a new computing infrastructure
The grid: blueprint for a new computing infrastructure
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
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Future Generation Computer Systems - Special issue on metacomputing
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ACM Computing Surveys (CSUR)
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Partitioning and Scheduling Parallel Programs for Multiprocessors
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IEEE Software
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IEEE Transactions on Parallel and Distributed Systems
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IEEE Transactions on Parallel and Distributed Systems
Adaptive Scheduling for Master-Worker Applications on the Computational Grid
GRID '00 Proceedings of the First IEEE/ACM International Workshop on Grid Computing
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IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
Simgrid: A Toolkit for the Simulation of Application Scheduling
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
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HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid?
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
A Task Duplication Based Scheduling Algorithm for Heterogeneous Systems
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
NP-complete scheduling problems
Journal of Computer and System Sciences
Guarantee the victorious probability of grid resources in the competition for finite tasks
GPC'08 Proceedings of the 3rd international conference on Advances in grid and pervasive computing
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This paper addresses the problem of scheduling parallel program tasks onto computational grid to minimize the execution time of the parallel program and the number of required processing elements. This task scheduling problem is known to be NP-complete. Existing scheduling algorithms either assume a fixed number of processing elements, or generate schedules that need more processing elements than necessary, which is especially obvious when using task duplication technique. To overcome the weaknesses, we propose a genetic scheduling algorithm using task duplication. The proposed algorithm can yield schedules with shorter execution time and fewer required processing elements, and without useless task duplications. The conditions under which the algorithm performs best were highlighted.