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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 donÂ¿t 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 about the processor utilization and the task workloads. This paper 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 doesnÂ¿t need any prediction information and can adapt to the grid environment. Finally, the scheduling system combines the proposed algorithm with the best of scheduling algorithms that need the prediction information. 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.