Scheduling parallel program tasks onto arbitrary target machines
Journal of Parallel and Distributed Computing - Special issue: software tools for parallel programming and visualization
IEEE Transactions on Parallel and Distributed Systems
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
Parallel Processing of Adaptive Meshes with Load Balancing
IEEE Transactions on Parallel and Distributed Systems
Topology preserving dynamic load balancing for parallel molecular simulations
SC '97 Proceedings of the 1997 ACM/IEEE conference on Supercomputing
A Family of Parallel Prefix Algorithms Embedded in Networks
IEEE Transactions on Parallel and Distributed Systems
A Genetic Algorithm for Multiprocessor Scheduling
IEEE Transactions on Parallel and Distributed Systems
A taxonomy of scheduling in general-purpose distributed computing systems
IEEE Transactions on Software Engineering
Journal of Parallel and Distributed Computing - Problems in parallel and distributed computing: Solutions based on evolutionary paradigms
IPDPS '00/JSSPP '00 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
A genetic-algorithm-based approach for subtask matching and scheduling in heterogeneous computing environments and a comparative study of parallel genetic algorithms
Appraising two decades of distributed computing theory research
Distributed Computing - Papers in celebration of the 20th anniversary of PODC
Computer Networks: A Systems Approach, 3rd Edition
Computer Networks: A Systems Approach, 3rd Edition
A memetic algorithm for reliability-based dynamic scheduling in heterogeneous computing environments
PDCS '07 Proceedings of the 19th IASTED International Conference on Parallel and Distributed Computing and Systems
Automation and Remote Control
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In this study, a heterogeneous computing environment is employed as a computational platform. In order to increase the efficiency of the system, a dynamic task-scheduling algorithm is proposed, which balances the load among the nodes of the system. The technique is dynamic, nonpreemptive, adaptive, and it uses a mixed centralised and decentralised policies. Based on the divide and conquer principle, the algorithm models the system as hypergrids and then balances the load among them. Recursively, the hypergrids of dimension k are divided into grids of dimensions k-1, until the dimension is 1. Then, all the nodes of the system are almost equally loaded. The optimum dimension of the hypergrid is chosen in order to achieve the best performance. The simulation results demonstrate the effectiveness of this technique. In addition, we determined the critical points representing lower bounds for which the algorithm should be effective and therefore should be triggered.