Performance-Aware Workflow Management for Grid Computing
The Computer Journal
Cost-Based Scheduling of Scientific Workflow Application on Utility Grids
E-SCIENCE '05 Proceedings of the First International Conference on e-Science and Grid Computing
Task scheduling strategies for workflow-based applications in grids
CCGRID '05 Proceedings of the Fifth IEEE International Symposium on Cluster Computing and the Grid (CCGrid'05) - Volume 2 - Volume 02
Stochastic Workflow Scheduling with QoS Guarantees in Grid Computing Environments
GCC '06 Proceedings of the Fifth International Conference on Grid and Cooperative Computing
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
Run-time Optimisation of Grid Workflow Applications
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
QoS Support For Workflows In A Volatile Grid
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
Metascheduling Multiple Resource Types using the MMKP
GRID '06 Proceedings of the 7th IEEE/ACM International Conference on Grid Computing
Grid load balancing using intelligent agents
Future Generation Computer Systems
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Recent enthusiasm in grid computing has resulted in a tremendous amount of research in resource scheduling techniques for tasks in a workflow. Most of the work on resource scheduling is aimed at minimizing the total response time for the entire workflow and treats the estimated response time of a task running on a local resource as a constant. In this paper, we propose a probabilistic framework for resource scheduling in grid environment that views the task response time as a probability distribution to take into consideration the uncertain factors. The goal is to dynamically assign resources to tasks so as to maximize the probability of completing the entire workflow within a desired total response time. We propose three algorithms for the dynamic resource scheduling in grid environment. Experimental results using synthetic data derived from a real protein annotation workflow application demonstrate that considering the uncertain factors of task response time in task scheduling does yield better performance, especially in a heterogeneous environment. We also compare the relative performance of the three proposed algorithms.