Hybrid Re-scheduling Mechanisms for Workflow Applications on Multi-cluster Grid
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Utility Driven Adaptive Work?ow Execution
CCGRID '09 Proceedings of the 2009 9th IEEE/ACM International Symposium on Cluster Computing and the Grid
Task migration enabling grid workflow application rescheduling
APWeb'08 Proceedings of the 10th Asia-Pacific web conference on Progress in WWW research and development
Adaptive service scheduling for workflow applications in Service-Oriented Grid
The Journal of Supercomputing
Failure-aware workflow scheduling in cluster environments
Cluster Computing
Queue waiting time aware dynamic workflow scheduling in multicluster environments
Journal of Computer Science and Technology
Online scheduling of workflow applications in grid environments
Future Generation Computer Systems
Scheduling concurrent workflows in HPC cloud through exploiting schedule gaps
ICA3PP'11 Proceedings of the 11th international conference on Algorithms and architectures for parallel processing - Volume Part I
Online scheduling of workflow applications in grid environment
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
A bi-criteria scheduling process with CoS support on grids and clouds
Concurrency and Computation: Practice & Experience
International Journal of Distance Education Technologies
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Workflow applications are gaining popularity in recent years because of the prevalence of cluster environments. Many algorithms have been developed since, however most static algorithms are designed in the problem domain of scheduling single workflow applications, thus not applicable to a common cluster environment where multiple workflow applications and other independent jobs compete for resources. Dynamic scheduling approaches can handle the mixed workload practically by nature but their performance has yet to optimize as they do not have a global view of workflow applications. Recent research efforts suggest merging multiple workflows into one workflow before execution, but fail to address an important issue that multiple workflow applications may be submitted at different times by different users. In this paper, we propose a planner-guided dynamic scheduling strategy for multiple workflow applications, leveraging job dependence information and execution time estimation.Our approach schedules individual jobs dynamically without requiring merging the workflow applications a priori. The simulation results show that the proposed algorithm significantly outperforms two other algorithms by 43.6% and 36.7% with respect to workflow makespan and turnaround time respectively, and it performs even better when the number of concurrent workflow applications increases and the resources are scarce.