优化异构云数据中心系统中的虚拟机部署

Q4 Computer Science
Aristide Ndayikengurukiye, Abderrahmane Ez-Zahout, F. Omary
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引用次数: 0

摘要

- 在云计算环境中,良好的资源管理仍然是云计算良好运行的一大挑战。在物理机上实施虚拟机放置(VMP)有助于实现各种目标,如资源分配、负载平衡、能源消耗和服务质量。云中的 VMP(虚拟机放置)至关重要,因此对其实施情况进行审核非常重要。它必须考虑到物理服务器的资源,包括 CPU、内存和存储。本文采用了一种基于灰狼优化(GWO)方法的元启发式算法来优化云环境中的虚拟机放置,从而有效地将用于托管虚拟服务器的活动虚拟机数量降至最低。实验结果证明了所提出的名为 "灰狼优化虚拟机放置(GWOVMP)"方法的有效性。与现有算法相比,该方法减少了 20.99% 的功耗和 1.80% 的资源浪费。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing Virtual Machines Placement in a Heterogeneous Cloud Data Center System
– In a cloud computing environment, good resource management remains a major challenge for its good operation. Implementing virtual machine placement (VMP) on physical machines helps to achieve various objectives, such as resource allocation, load balancing, energy consumption, and quality of service. VMP (virtual machine placement) in the cloud is critical, so it's important to audit its implementation. It must take into account the resources of the physical server, including CPU, RAM, and storage. In this paper, a metaheuristic algorithm based on the Grey Wolf Optimization (GWO) method is used to optimize the placement of virtual machines in a cloud environment, effectively minimizing the number of active virtual machines used to host virtual servers. Experimental results demonstrate the effectiveness of the proposed method, called Grey Wolf Optimization for Virtual Machine Placement (GWOVMP). The method reduces power consumption by 20.99 and resource wastage by 1.80 compared with existing algorithms.
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来源期刊
International Journal of Computer Networks and Applications
International Journal of Computer Networks and Applications Computer Science-Computer Science Applications
CiteScore
2.30
自引率
0.00%
发文量
40
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