人工智能驱动的虚拟机选择:通过蝶算法扩大云性能
Sindhu Rashmi1, Vikas Siwach1, Harkesh Sehrawat1
1UIET, MDU Rohtak, India.
Heliyon
|October 10, 2024
概括
本研究介绍了Dragonfly算法与修改的最佳合适度减小 (DA-MBFD) 集成,用于在云计算中高效地放置虚拟机. DA-MBFD显著降低了功耗和虚拟机迁移.
科学领域:
- 云计算 云计算 云计算
- 虚拟化技术是虚拟化的技术.
- 资源管理 资源管理
背景情况:
- 云计算的普及需要高效的资源管理.
- 云环境中的虚拟化需要仔细处理资源和能源.
- 越来越多的虚拟机 (VM) 请求对云数据中心构成了挑战.
研究的目的:
- 提出一种用于优化虚拟机放置的新算法.
- 为了最大限度地减少云数据中心的电力消耗和VM迁移计数.
- 为了提高云资源利用的效率.
主要方法:
- 引入了 Dragonfly 算法与修改的最佳合适度下降 (DA-MBFD) 集成.
- 对于VM资源需求排名,使用了修改的最佳合适度下降 (MBFD).
- 员工迁移最小化 (MM) 用于热点检测和龙算法 (DA) 用于VM更换优化.
主要成果:
- DA-MBFD显示了电力消耗的显著降低 (高达8.6%).
- 在服务级别协议 (SLA) 违规方面取得了显著的下降 (高达9.25%).
- 显示了VM迁移数量的减少 (高达8.92%).
结论:
- 与现有的VM安置技术相比,DA-MBFD提供了更高的性能.
- 拟议的算法有效地平衡了资源利用和能源效率.
- 在大规模的云环境中,DA-MBFD为管理VM安置提供了一个有前途的解决方案.
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