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针对网格环境下计算节点的自治性、异构性、分布性等特征,提出一种基于任务响应时间的动态修正预测和任务流整形的网格调度算法,该调度方法依据历史数据和最近访问过计算节点的任务请求提交时间、任务完成时间、网络通信延迟等信息,预测计算节点的将来任务响应时间,将任务提交给预测的轻负载或性能较优的计算节点完成。通过使用动态修正算法和任务流整形算法降低预测误差,提高资源利用率。实验结果表明,该方法在任务响应时间、任务的吞吐率等方面优于随机调度等传统算法,具有较好的综合性能。
Aiming at the autonomy, heterogeneity and distribution of computing nodes in grid environment, this paper proposes a grid scheduling algorithm based on task response time dynamic correction prediction and task flow shaping. The scheduling method based on historical data and recent access After calculating the task request submission time, task completion time and network communication delay of the computing node, the task response time of the computing node is predicted, and the task is submitted to the predicted light load or the better performing computing node. Through the use of dynamic correction algorithm and task stream shaping algorithm to reduce the prediction error and improve resource utilization. Experimental results show that the proposed method is superior to traditional algorithms such as stochastic scheduling in terms of task response time and task throughput, and has better overall performance.