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Task Migration Enabling Grid Workflow Application Rescheduling

机译:任务迁移启用网格工作流应用程序重新安排

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This paper focuses on the task migration enabling grid workflow application rescheduling problem, presents a reduced task graph model, and implements a performance oriented rescheduling algorithm based on immune genetic algorithm. The experiment shows that, compared with Adaptive Heterogeneous Earliest Finish Time static rescheduling algorithm and the classical dynamic Max-Min scheduling algorithm, the performance advantage of the proposed rescheduling algorithm is obvious, on the one hand because of the performance contribution of global optimization and task migration, and on the other hand because of the efficiency contribution of task graph reduction and immune genetic algorithm's convergent speed. It also shows that task migration improves grid application's adaptability of dynamics further.
机译:本文着重研究了支持网格工作流应用程序重新调度的任务迁移问题,提出了一种简化的任务图模型,并基于免疫遗传算法实现了一种面向性能的重新调度算法。实验表明,与自适应异构最早完成静态重调度算法和经典的动态Max-Min调度算法相比,所提出的重调度算法的性能优势明显,一方面是由于全局优化和任务的性能贡献迁移,另一方面是由于任务图缩减的效率贡献和免疫遗传算法的收敛速度。它还表明,任务迁移进一步提高了网格应用程序动力学的适应性。

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