基于改进粒子群算法的云计算虚拟机部署策略

IF 0.7 4区 计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Computing and Informatics Pub Date : 2020-03-24 DOI:10.31577/cai_2020_1-2_83
Shanchen Pang, Dong Dekun, Shuyu Wang
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引用次数: 2

摘要

能源消耗是计算能力增长驱动的重要成本,因此节能已成为云系统面临的主要问题之一。如何在物理机资源阈值的约束下最大限度地利用物理机,减少虚拟机迁移次数,保持负载平衡,是实现数据中心节能的有效途径。在本文中,我们提出了一个用于虚拟机部署的多目标物理模型。然后将改进的多目标粒子群优化算法(TPSO)应用于虚拟机部署。与其他算法相比,该算法在初始阶段具有更好的遍历性,提高了粒子群的优化精度和优化效率。基于CloudSim仿真平台的实验结果表明,该算法在提高物理机资源利用率、减少资源浪费、提高系统负载平衡方面是有效的。
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Virtual Machine Deployment Strategy Based on Improved PSO in Cloud Computing
Energy consumption is an important cost driven by growth of computing power, thereby energy conservation has become one of the major problems faced by cloud system. How to maximize the utilization of physical machines, reduce the number of virtual machine migrations, and maintain load balance under the constraints of physical machine resource thresholds that is the effective way to implement energy saving in data center. In the paper, we propose a multi-objective physical model for virtual machine deployment. Then the improved multi-objective particle swarm optimization (TPSO) is applied to virtual machine deployment. Compared to other algorithms, the algorithm has better ergodicity into the initial stage, improves the optimization precision and optimization efficiency of the particle swarm. The experimental results based on CloudSim simulation platform show that the algorithm is effective at improving physical machine resource utilization, reducing resource waste, and improving system load balance.
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来源期刊
Computing and Informatics
Computing and Informatics 工程技术-计算机:人工智能
CiteScore
1.60
自引率
14.30%
发文量
19
审稿时长
9 months
期刊介绍: Main Journal Topics: COMPUTER ARCHITECTURES AND NETWORKING PARALLEL AND DISTRIBUTED COMPUTING THEORETICAL FOUNDATIONS SOFTWARE ENGINEERING KNOWLEDGE AND INFORMATION ENGINEERING Apart from the main topics given above, the Editorial Board welcomes papers from other areas of computing and informatics.
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