一个哈里斯·霍克优化系统,用于在云数据中心中节能和资源高效地放置虚拟机
Madhusudhan H S1, Satish Kumar T2, Punit Gupta3
1Department of Computer Science & Engineering, Vidyavardhaka College of Engineering, Mysuru, Karnataka, India.
本研究介绍了哈里斯·霍克优化 (HHO) 模型,用于在云数据中心高效地放置虚拟机. HHO模型显著降低了电力消耗,并改善了资源利用率,以优化云基础设施.
科学领域:
- 计算机科学 计算机科学
- 云计算 云计算 云计算 云计算
- 优化算法 优化算法
背景情况:
- 由于服务迁移和扩展基础设施,云数据中心面临着日益增长的能源需求.
- 高效的资源配置对于优化云环境中的电力使用和运营成本至关重要.
- 虚拟机放置策略直接影响云资源利用和能源消耗.
研究的目的:
- 介绍一种使用哈里斯客优化 (HHO) 模型的新型虚拟机放置技术.
- 为了优化虚拟机的放置,减少云数据中心主机的负载和功耗.
- 评估HHO模型与动态云环境中的现有方法的有效性.
主要方法:
- 开发了一个基于哈里斯霍克优化 (HHO) 模型的虚拟机放置技术.
- 利用PlanetLab的实时工作负载跟踪进行绩效评估.
- 将HHO模型与粒子群优化 (PSO) 和最佳合适减小 (BFD) 算法进行比较.
主要成果:
- HHO算法将执行时间提高了4%,资源利用率提高了17%.
- 实现了27%的电力消耗降低和16%的服务水平协议 (SLA) 违规减少.
- 通过模拟场景,在处理负载不足和负载过重的条件中表现出卓越的性能.
结论:
- 拟议的HHO模型提供了一种高效和有效的解决方案,用于将虚拟机放置在云数据中心.
- 由于其在动态和不确定的环境中的有效性,HHO适合于现实世界的云基础设施.
- 使用HHO优化虚拟机的放置可以节省大量的能源,并提高运营效率.
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