Guided self-scheduling: A practical scheduling scheme for parallel supercomputers
IEEE Transactions on Computers
Factoring: a method for scheduling parallel loops
Communications of the ACM
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Simgrid: A Toolkit for the Simulation of Application Scheduling
CCGRID '01 Proceedings of the 1st International Symposium on Cluster Computing and the Grid
A Class of Loop Self-Scheduling for Heterogeneous Clusters
CLUSTER '01 Proceedings of the 3rd IEEE International Conference on Cluster Computing
Scheduling divisible workloads on heterogeneous platforms
Parallel Computing - Parallel matrix algorithms and applications (PMAA '02)
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Efficient scheduling of Bag-of-Tasks (BoT) applications in a computational grid environment reveals several challenges due to its high heterogeneity, dynamic behavior, and space shared utilization. Currently, most of the scheduling algorithms proposed in the literature use a master-oriented algorithm, in which the master is the only responsible for choosing the best task size to send to each slave. We present in this paper a different approach whose main originality is to be slave-oriented, ie each slave locally determines, from a set of initial runs, which workload size is more adapted to its capacities and notifies the master of it. Finally, we show some measurements comparing our algorithm with other three well-known scheduling algorithms using the SimGrid toolkit.