电信科学 ›› 2022, Vol. 38 ›› Issue (3): 172-182.doi: 10.11959/j.issn.1000-0801.2022060
• 研究与开发 • 上一篇
徐胜超1, 熊茂华1, 周天绮2
修回日期:
2022-01-24
出版日期:
2022-03-20
发布日期:
2022-03-01
作者简介:
徐胜超(1980- ),男,广州华商学院数据科学学院讲师,主要研究方向为并行分布式处理软件基金资助:
Shengchao XU1, Maohua XIONG1, Tianqi ZHOU2
Revised:
2022-01-24
Online:
2022-03-20
Published:
2022-03-01
Supported by:
摘要:
利用虚拟机放置策略对云数据中心的物理资源利用效率进行优化十分必要。提出了基于萤火虫群优化的虚拟机放置(glowworm swarm optimization based VM placement,Gso-wmp)方法。GSO-VMP方法将物理主机的处理器使用效率表示为荧光素值,当一个虚拟机被放置到物理主机上时,该物理主机的荧光素值都要进行更新;能够在局部径向范围内搜索到更多的可用物理主机,完成虚拟机放置,减少了虚拟机的迁移次数,从而间接地节省了物理主机的能量消耗。使用CloudSim作为GSO-VMP的仿真环境进行仿真,实验结果表明,GSO-VMP方法使得云数据中心的能耗降低、多维物理资源利用率提高。
中图分类号:
徐胜超, 熊茂华, 周天绮. 基于萤火虫群优化的虚拟机放置方法[J]. 电信科学, 2022, 38(3): 172-182.
Shengchao XU, Maohua XIONG, Tianqi ZHOU. Approach of glowworm swarm optimization based virtual machine placement[J]. Telecommunications Science, 2022, 38(3): 172-182.
表2
不同优化策略性能分析相关的参数设置"
算法 | 参数 | 具体值 |
GSO-VM | p荧光素衰减系数(0<p<1) | 0.4 |
物理主机 j的局部径向范围的最大传感范围ωs | 0.6 | |
相邻区域的变化率 θ | 0.08 | |
最佳的邻居的数目kt | 5 | |
移动的步长尺寸s | 0.03 | |
荧光素初始值 | 0.05 | |
FPA[ | 种群大小 | 50, 100, 150, 200 |
标准函数gamma | 1.5 | |
随机步长 L | [0, 1] | |
转换概率 p | 0.9 | |
最大迭代次数 | 1 000 | |
GA[ | 种群大小 | 50, 100, 150, 200 |
交叉率 | 0.5 | |
变异率 | 0.01 | |
最大迭代次数 | 1 000 | |
ACO[ | 蚁群算法的蚂蚁个数 | 50, 100, 150, 200 |
挥发因子 p | 0.4 | |
信息素追踪权重α | 0.3 | |
启发式信息权重β | 1 | |
信息素更新常量 Q | 100 | |
最大迭代次数 | 1 000 |
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