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Title page for etd-0703106-154758


URN etd-0703106-154758 Statistics This thesis had been viewed 2392 times. Download 1480 times.
Author Chin-Hsiian Liang
Author's Email Address No Public.
Department Computer Science and Enginerring
Year 2005 Semester 2
Degree Master Type of Document Master's Thesis
Language English Page Count 43
Title An Adaptive Task Scheduling System for Grid Computing
Keyword
  • Grid Computing
  • Dynamic Scheduling
  • Dynamic Scheduling
  • Grid Computing
  • Abstract In order to efficiently utilize available grid resources and promptly complete tasks assigned to the grid, providing a suitable job scheduling strategy for the grid computing is necessary. Lots of grid scheduling algorithms have already been developed, and some of them are used to schedule independent coarse-grained tasks. Those algorithms do not adapt very well to the grid tasks that are submitted continuously and randomly. Besides, they mostly need a prediction system to provide the prediction information of the processor utilization and the task workloads. This thesis proposes an adaptive grid scheduling system for high-throughput applications. Firstly, a grid scheduling model is adopted to represent the performance of processors, the task workloads, and the schedules. Then we develop a scheduling algorithm that does not need any prediction information and can adapt to the grid environment. The proposed scheduling system can be used with the suitable of scheduling algorithms that need the prediction information if necessary. According to the accuracy of the prediction system in the grid, the system selects the proper strategy to schedule tasks. A prototype of this model is developed and tested with several experiments. The experimental results of the simulation show that the proposed scheduling system is able to perform scheduling well in the grid environment.
    Advisor Committee
  • Liang-Teh Lee - advisor
  • Ge-Ming Chiu - co-chair
  • Yo-Ping Huang - co-chair
  • Files indicate in-campus access immediately and off-campus access at one year
    Date of Defense 2006-06-16 Date of Submission 2006-07-03


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