Journal of Parallel and Distributed Computing
On unit task linear-nonlinear two-cluster scheduling problem
Proceedings of the 2005 ACM symposium on Applied computing
Bi-objective scheduling algorithms for optimizing makespan and reliability on heterogeneous systems
Proceedings of the nineteenth annual ACM symposium on Parallel algorithms and architectures
Reactive grid scheduling of DAG applications
PDCN'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: parallel and distributed computing and networks
Ant colony optimization for precedence-constrained heterogeneous multiprocessor assignment problem
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Scheduling multiple DAGs onto heterogeneous systems
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
A task duplication based bottom-up scheduling algorithm for heterogeneous environments
IPDPS'06 Proceedings of the 20th international conference on Parallel and distributed processing
Incremental placement of interactive perception applications
Proceedings of the 20th international symposium on High performance distributed computing
Scheduling for heterogeneous Systems using constrained critical paths
Parallel Computing
Stochastic DAG scheduling using a Monte Carlo approach
Journal of Parallel and Distributed Computing
NewsCast: an adaptive video stream production and delivery system
CASCON '13 Proceedings of the 2013 Conference of the Center for Advanced Studies on Collaborative Research
Distributed workflow mapping algorithm for maximized reliability under end-to-end delay constraint
The Journal of Supercomputing
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Abstract: The goal of the OURAGAN project is to provide access of meta-computing resources to Scilab users. We present here an approach that consists, given a Scilab script, in scheduling and executing this script on an heterogeneous cluster of machines. One of the most effective scheduling technique is called clustering which consists in grouping tasks on virtual processors (clusters) and then mapping clusters onto real processors. In this paper, we study and apply the clustering technique for heterogeneous systems. We present a clustering algorithm called triplet, study its performance and compare it to the HEFT algorithm. We show that triplet has good characteristics and outperforms HEFT in most of the cases.