张水平, 仲伟彪. 基于元胞蚁群算法模型的云资源调度[J]. 微电子学与计算机, 2015, 32(8): 54-57. DOI: 10.19304/j.cnki.issn1000-7180.2015.08.011
引用本文: 张水平, 仲伟彪. 基于元胞蚁群算法模型的云资源调度[J]. 微电子学与计算机, 2015, 32(8): 54-57. DOI: 10.19304/j.cnki.issn1000-7180.2015.08.011
ZHANG Shui-ping, ZHONG Wei-biao. Cloud Resource Schedule Based on Cellular Ant Colony Optimization[J]. Microelectronics & Computer, 2015, 32(8): 54-57. DOI: 10.19304/j.cnki.issn1000-7180.2015.08.011
Citation: ZHANG Shui-ping, ZHONG Wei-biao. Cloud Resource Schedule Based on Cellular Ant Colony Optimization[J]. Microelectronics & Computer, 2015, 32(8): 54-57. DOI: 10.19304/j.cnki.issn1000-7180.2015.08.011

基于元胞蚁群算法模型的云资源调度

Cloud Resource Schedule Based on Cellular Ant Colony Optimization

  • 摘要: 针对传统蚁群算法易陷于局部最优解的特点,提出了一种基于元胞自动机模型的改进蚁群算法—元胞蚁群算法.该算法通过元胞自动机本身的演化机制对蚂蚁寻找食物得到的最优解进行二次分配,大大改善了算法的收敛速度,并在CloudSim仿真平台上运用该算法进行资源调度,分析算法的调度性能.结果表明,新算法能有效缩短调度所用的时间,提高了调度的效率,满足云环境下资源调度的要求.

     

    Abstract: Ant Colony Optimization(ACO) has successfully solved series of discrete optimization problems. However its global conver-gence is not fully studied and proved. This paper proposed an improved Ant Colony Optimization based on cellular automata——Cellular Ant Colony Optimization(CACO).Through redistributing the optimal solution, the algorithm is improved on the speed of convergence. After comparing it with traditional Ant Colony Optimization(ACO), Round Robin under the simulator platform CloudSim, the experiment shows that the algorithm could reduce the whole makespan and decrease the costs of users, which is an effective resource schedule satisfying cloud environment.

     

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