Learning automata: an introduction
Learning automata: an introduction
The grid
Heuristic Algorithms for Scheduling Independent Tasks on Nonidentical Processors
Journal of the ACM (JACM)
Dynamic mapping of a class of independent tasks onto heterogeneous computing systems
Journal of Parallel and Distributed Computing - Special issue on software support for distributed computing
Optimal Schedules for Cycle-Stealing in a Network of Workstations with a Bag-of-Tasks Workload
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Distributed Dynamic Scheduling of Composite Tasks on Grid Computing Systems
IPDPS '02 Proceedings of the 16th International Parallel and Distributed Processing Symposium
A High-Performance Mapping Algorithm for Heterogeneous Computing Systems
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
SRDS '98 Proceedings of the The 17th IEEE Symposium on Reliable Distributed Systems
Heuristic scheduling for bag-of-tasks applications in combination with QoS in the computational grid
Future Generation Computer Systems - Special issue: Advanced grid technologies
Distributing MCell Simulations on the Grid
International Journal of High Performance Computing Applications
A new fine-grained evolutionary algorithm based on cellular learning automata
International Journal of Hybrid Intelligent Systems
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Computational grid provides a platform for exploiting various computational resources over wide area networks. One of the concerns in implementing computational grid environment is how to effectively map tasks onto resources in order to gain high utilization in the highly heterogeneous environment of the grid. In this paper, three algorithms for task mapping based on learning automata are introduced. To show the effectiveness of the proposed algorithms, computer simulations have been conducted. The results of experiments show that the proposed algorithms outperform two best existing mapping algorithms when the heterogeneity of the environment is very high.