Journal of Parallel and Distributed Computing - Special issue on parallel evolutionary computing
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
Journal of Parallel and Distributed Computing
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
IEEE Transactions on Parallel and Distributed Systems
IEEE Transactions on Parallel and Distributed Systems
A Dynamic Matching and Scheduling Algorithm for Heterogeneous Computing Systems
HCW '98 Proceedings of the Seventh Heterogeneous Computing Workshop
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
A Unified Resource Scheduling Framework for Heterogeneous Computing Environments
HCW '99 Proceedings of the Eighth Heterogeneous Computing Workshop
Heuristics for Scheduling Parameter Sweep Applications in Grid Environments
HCW '00 Proceedings of the 9th Heterogeneous Computing Workshop
Performance-Aware Workflow Management for Grid Computing
The Computer Journal
Relative Performance of Scheduling Algorithms in Grid Environments
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
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Job scheduling plays a critical role in the utilisation of grid resources by mapping a number of jobs to grid resources. However, the heterogeneity of grid resources adds some challenges to the work of job scheduling, especially when jobs have dependencies which can be represented as Direct Acyclic Graphs DAGs. It is widely recognised that scheduling m jobs to n resources with an objective to achieve a minimum makespan has shown to be NP-complete, requiring the development of heuristics. Although a number of heuristics are available for job scheduling optimisation, selecting the best heuristic to use in a given grid environment remains a difficult problem due to the fact that the performance of each original heuristic is usually evaluated under different assumptions. This paper evaluates 12 representative heuristics for dependent job scheduling under one set of common assumptions. The results are presented and analysed, which provides an even basis in comparison of the performance of those heuristics. To facilitate performance evaluation, a DAG simulator is implemented which provides a set of tools for DAG job configuration, execution, and monitoring. The components of the DAG simulator are also presented in this paper.