自适应邻域的多目标网格任务调度算法研究

针对网格计算中的多目标网格任务调度问题,提出了一种基于自适应邻域的多目标网格任务调度算法。该算法通过求解多个网格任务调度目标函数的非劣解集,采用自适应邻域的方法来保持网格任务调度多目标解集的分布性,尝试解决网格任务调度中多目标协同优化问题。实验结果证明,该算法能够有效地平衡时间维度和费用维度目标,提高了资源的利用率和任务的执行效率,与Min-min和Max-min算法相比具有较好的性能。

Multi-objective evolutionary algorithm for grid job scheduling based on adaptive neighborhood

A new adaptive neighborhood Multi-Objective Grid Task Scheduling Algorithm(ANMO-GTSA) was proposed in this paper for the multi-objective job scheduling collaborative optimization problem in grid computing.In the ANMO-GTSA,an adaptive neighborhood method was applied to find the non-inferior set of solutions and maintain the diversity of the multi-objective job scheduling population.The experimental results indicate that the algorithm proposed in this paper can not only balance the multi-objective job scheduling,but also improve the resource utilization and efficiency of task execution.Moreover,the proposed algorithm can achieve better performance on time-dimension and cost-dimension than the traditional Min-min and Max-min algorithms.