东北大学学报（自然科学版）

东北大学学报(自然科学版) ›› 2010, Vol. 31 ›› Issue (5): 630-633.DOI: -

• 论著 • 上一篇    下一篇

一种基于蚁群算法动态均衡的网格任务调度

孙大为;常桂然;陈东;王兴伟;   

  1. 东北大学信息科学与工程学院;
  • 收稿日期:2013-06-20 修回日期:2013-06-20 出版日期:2010-05-15 发布日期:2013-06-20
  • 通讯作者: -
  • 作者简介:-
  • 基金资助:
    国家自然科学基金资助项目(60673159,70671020);;

A grid task scheduling with dynamic equilibrium based on ant colony algorithm

Sun, Da-Wei (1); Chang, Gui-Ran (1); Chen, Dong (1); Wang, Xing-Wei (1)   

  1. (1) School of Information Science and Engineering, Northeastern University, Shenyang 110004, China
  • Received:2013-06-20 Revised:2013-06-20 Online:2010-05-15 Published:2013-06-20
  • Contact: Sun, D.-W.
  • About author:-
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摘要： 网格资源分配属于NP-难问题,为了更好地解决该问题,首先建立一种性能QoS优化的作业级网格任务调度模型和目标函数,并对资源和任务数进行了分析.提出了基于动态信誉度的改进蚁群算法RACO(reputation-based ACO)进行网格任务调度,RACO引入空间效率和时间效率的动态调节因子,同时采用局部和全局信息素更新策略.仿真实验表明,RACO在资源利用率、动态均衡方面优于Min-min,Max-min和ACO算法.

关键词: 网格计算, 任务调度, 动态均衡, 蚁群算法, 信誉

Abstract: Resource allocation in grid is an NP-hard problem. To optimize the grid system, a performance QoS optimization model is developed for grid task scheduling and objective function, with the number of resources and tasks analyzed in detail. Then, an improved ant colony algorithm named RACO(reputation-based ant colony algorithm) is presented to schedule tasks in grid, based on the dynamic reputation. Introducing a dynamic scheduling factor involving both space and time efficiencies, a local and global pheromone updating strategy is applied to RACO. Simulation results showed that RACO algorithm outperforms the conventional Min-min, Max-min and ACO in resource utilization rate and dynamic equilibrium.

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