计算网格上基于爬坡的分散作业调度

Qingjiang Wang, Yun Gao, Peishun Liu
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引用次数: 16

摘要

分散式作业调度是通过相邻网格节点之间的作业迁移来实现的。为了优化新提交作业的节点选择,作业可能会被迁移多次。这里采用爬坡法确定迁徙路线。实验模拟分散式作业调度,包括节点邻接、网格节点的本地调度和网格工作负载。与k分布和拍卖方法相比,基于爬坡的调度通常可以提高处理器利用率,并且可以减少有界减速
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hill Climbing-Based Decentralized Job Scheduling on Computational Grids
De-centralized job scheduling is implemented by job migrations between neighboring grid nodes. To optimize node selection of a new-submitted job, the job may be migrated many times. Here, the hill climbing method is used to determine the migration route. Experiments simulate de-centralized job scheduling, including node adjacencies, local scheduling of grid nodes, and grid workload. Compared with k-distributed and auction methods, hill climbing-based scheduling usually can enhance processor utilization, and can reduce bounded slowdown
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