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
Observations on Using Genetic Algorithms for Dynamic Load-Balancing
IEEE Transactions on Parallel and Distributed Systems
Journal of Parallel and Distributed Computing
Experiments with Scheduling Using Simulated Annealing in a Grid Environment
GRID '02 Proceedings of the Third International Workshop on Grid Computing
A High-Performance Mapping Algorithm for Heterogeneous Computing Systems
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Framework for Task Scheduling in Heterogeneous Distributed Computing Using Genetic Algorithms
Artificial Intelligence Review
Immediate mode scheduling in grid systems
International Journal of Web and Grid Services
Batch mode scheduling in grid systems
International Journal of Web and Grid Services
Adaptive grid job scheduling with genetic algorithms
Future Generation Computer Systems
Scheduling jobs on computational grids using fuzzy particle swarm algorithm
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part II
Computers & Mathematics with Applications
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Scheduling in large scale distributed computing environments such as Computational Grids, is currently receiving a considerable attention of researchers. Despite that scheduling in such systems has much in common with scheduling in traditional distributing systems, the new characteristics of Grid systems make the problem more complex and versatile to match different needs of Grid-enabled applications. In this work, by conceiving scheduling problem as a family of problems, we first identify most common versions of the scheduling problem based on six dimensions: type of the environment, architecture type of the scheduler, immediacy of the processing, type of interrelations among tasks, type of preemptive policy and type of optimization model. Then, we review different families of heuristic methods used for the resolution of the problem, including ad hoc methods, local search methods and population-based methods.