Optimization of Node Selections for Grid Jobs under De-Centralized Scheduling Frameworks

WANG Qing-jiang; XU Jian-liang

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Optimization of Node Selections for Grid Jobs under De-Centralized Scheduling Frameworks

更新时间：2025-07-16

- Optimization of Node Selections for Grid Jobs under De-Centralized Scheduling Frameworks
- Acta Electronica Sinica Vol. 34, Issue 8, Pages: 1420-1423(2006)
- 作者机构：
  
  中国海洋大学计算机科学系,山东,青岛,266071
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP393
- Published：2006
- 稿件说明：
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WANG Qing-jiang, XU Jian-liang. Optimization of Node Selections for Grid Jobs under De-Centralized Scheduling Frameworks[J]. Acta Electronica Sinica, 2006, 34(8): 1420-1423.
DOI：

WANG Qing-jiang, XU Jian-liang. Optimization of Node Selections for Grid Jobs under De-Centralized Scheduling Frameworks[J]. Acta Electronica Sinica, 2006, 34(8): 1420-1423. DOI：

摘要

为优化无中心式调度框架下网格作业的节点选择

提出了随机多起点爬山算法.为使多个起点均匀分布于网格

按随机选择邻居的重复次数的指数增长找出各起点.为反映合理的用户调度需求

用平均的并行计算能力加权的有界减慢率衡量节点选择.灵活调整网格工作负荷

对随机多起点爬山算法进行了全面评估.在网格负载不是很轻情况下

该算法能有效地在网格全局优化节点选择.

Abstract

To optimize node selections for grid jobs under de-centralized scheduling frameworks

the algorithm of random multi-start hill climbing is proposed.To make multiple start-points distributed uniformly in grids

each start-point is found according to exponential increase of iteration times of selecting neighbors randomly.To reflect reasonable scheduling requirements from users

the average bounded slowdown weighted by parallel computation capacity is used to evaluate node selections.By flexibly adjusting grid workload

the algorithm of random multi-start hill climbing is evaluated comprehensively.With grid load being not too light

the algorithm can effectively optimize node selections in the whole grid.

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Related Institution

State Key Laboratory of Information Security,Institute of Software,The Chinese Academy of Sciences

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State Key Laboratory of Information SecurityInstitute of SoftwareThe Chinese Academy of SciencesBeijing 100080China

Graduate School of Chinese Academy of SciencesBeijing 100049China

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