Journal of Parallel and Distributed Computing
Performance-Effective and Low-Complexity Task Scheduling for Heterogeneous Computing
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
GridAnt: A Client-Controllable Grid Work.ow System
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 7 - Volume 7
ACSW Frontiers '05 Proceedings of the 2005 Australasian workshop on Grid computing and e-research - Volume 44
Scheduling of scientific workflows in the ASKALON grid environment
ACM SIGMOD Record
Scheduling Data-IntensiveWorkflows onto Storage-Constrained Distributed Resources
CCGRID '07 Proceedings of the Seventh IEEE International Symposium on Cluster Computing and the Grid
Scheduling strategies for mapping application workflows onto the grid
HPDC '05 Proceedings of the High Performance Distributed Computing, 2005. HPDC-14. Proceedings. 14th IEEE International Symposium
Globus toolkit version 4: software for service-oriented systems
NPC'05 Proceedings of the 2005 IFIP international conference on Network and Parallel Computing
Adaptable scheduling algorithm for grids with resource redeployment capability
ICA3PP'10 Proceedings of the 10th international conference on Algorithms and Architectures for Parallel Processing - Volume Part I
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DAG has been extensively used in grid workflow modeling. Since the computational capacity of available grid resources tends to be heterogeneous, efficient and effective workflow job scheduling becomes essential. It poses great challenges to achieve minimum job accomplishing time while maintaining high grid resources utilization efficiency. Based on list scheduling and group scheduling, in this paper we propose a novel static scheduling heuristic, called DAGMap. DAGMap consists of three phases, namely prioritizing, grouping, and independent task scheduling. Three salient features of DAGMap are 1) Task grouping is based on dependency relationships and task upward priority; 2) Critical tasks are scheduled first; and 3) Min-Min and Max-Min selective scheduling are used for independent tasks. The experimental results show that DAGMap can achieve better performance than other previous algorithms in terms of makespan, speedup, and efficiency.