基于萤火虫群优化的虚拟机放置方法

徐胜超; 熊茂华; 周天绮

doi:10.11959/j.issn.1000-0801.2022060

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基于萤火虫群优化的虚拟机放置方法

研究与开发 | 更新时间：2024-06-05

- 基于萤火虫群优化的虚拟机放置方法
- Approach of glowworm swarm optimization based virtual machine placement
- 电信科学 2022年38卷第3期页码：172-182
- 作者机构：
  
  1. 广州华商学院数据科学学院，广东广州 511300
  2. 浙江药科职业大学医疗器械学院，浙江宁波 315100
- 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" ]
  [ "熊茂华（1958- ），男，广州华商学院数据科学学院教授、硕士生导师，主要研究方向为嵌入式与物联网、智能控制、人工智能技术" ]
  [ "周天绮（1976- ），男，浙江药科职业大学医疗器械学院副教授，主要研究方向为图像处理、医疗大数据和云计算" ]
- 基金信息：
  
  广东省高等学校科学研究特色创新项目;Characteristic Innovation Project of Scientific Research in Colleges and Universities of Guangdong Province(2021KTSCX167);广州华商学院校内导师制科研项目;Science and Research Project in Supervisor of Guangzhou Huashang College(2021HSDS15)
- DOI：10.11959/j.issn.1000-0801.2022060
  中图分类号： TP393
- 网络出版日期：2022-03，
  
  纸质出版日期：2022-03-20
- 稿件说明：
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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.
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. DOI： 10.11959/j.issn.1000-0801.2022060.

Shengchao XU, Maohua XIONG, Tianqi ZHOU. Approach of glowworm swarm optimization based virtual machine placement[J]. Telecommunications science, 2022, 38(3): 172-182. DOI： 10.11959/j.issn.1000-0801.2022060.

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。

Abstract

In a cloud data center

one of the most important problems is using novel virtual machine placement strategy to promote the physical resource utilization.An approach of glowworm swarm optimization based virtual machine placement for cloud data centers called GSO-VMP was proposed.In the virtual placement

GSO algorithm was used to find a near-optimal solution.Each physical host had a luciferin value which represented the available CPU utilization.Whenever a VM was placed to a physical host

luciferin value of this physical host was updated.GSO-VMP algorithm could search the more available physical host within local range and thus the virtual migration numbers had been decreased and low energy consumption had been obtained.GSO-VMP had been evaluated using CloudSim with real-world workload data.The experimental results show that GSO-VMP has good performance in resource wastage and energy consumption.

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