.png){kind=icon}
返回
An energy-efficient algorithm for virtual machine placement optimization in cloud data centers
.png){kind=icon}
PRE
AI
DOI：10.1007/s10586-020-03096-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Cloud providers offer computing services based on user demands using the Infrastructure as a Service (IaaS) service model. In a cloud data center, it is possible that multiple Virtual Machines (VMs) run on a Physical Machine (PM) using virtualization technology. Virtual Machine Placement (VMP) problem is the mapping of virtual machines across multiple physical ones. This process plays a vital role in defining energy consumption and resource usage efficiency in the cloud data center infrastructure. However, providing an efficient solution is not trivial due to difficulties such as machine heterogeneity, multi-dimensional resources, and large scale cloud data centers. In this paper, we propose an efficient heuristic algorithm that focuses on power consumption and resource wastage optimization to solve the aforementioned problem. The proposed algorithm, called MinPR, minimizes the total power consumption by reducing the number of active physical machines and prioritizing the power-efficient ones. Also, it reduces resource wastage by maximizing and balancing resource utilization among physical machines. To achieve these goals, we propose a new Resource Usage Factor model that manages virtual machine placement on physical machines using reward and penalty mechanisms. Simulations based on cloud user-customized VMs and Amazon EC2 Instances workloads illustrate that the proposed algorithm outperforms existing approaches. In particular, the proposed algorithm reduces total energy consumption by up to 15% for cloud user-customized VMs and by up to 10% for Amazon EC2 Instances.
Keyword:
Cloud computing
Infrastructure as a service (IaaS)
Virtual machine placement (VMP)
Optimization
Energy efficiency
Resource utilization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
C
IF:
4.1
论文数:
5.0K
被引数:
7.5K
机构
引用论文
.png){kind=icon}
RELENTLESS RELAPSE: A CASE OF RECURRENT IDIOPATHIC MEMBRANOUS NEPHROPATHY (MN) IN A KIDNEY TRANSPLANT RECIPIENT顽固性复发：一例肾移植受者中复发性特发性膜性肾病（MN）的病例
PREAI
分享收藏Use of Landsat and SRTM Data to Detect Broad-Scale Biodiversity Patterns in Northwestern Amazonia
OAAI
OAAI分享收藏
查看更多内容