利用遗传算法完成虚拟机放置策略的优化

摘要/Abstract

摘要： 提出基于遗传算法的虚拟机放置方法GA-VMP(Genetic Algorithm based Virtual Machine Placement)。GA-VMP是一种应用于虚拟机迁移过程的优化算法。在物理主机状态检测和虚拟机选择阶段分别选取了鲁棒局部归约检测方法和最小迁移时间选择方法；在最后的虚拟机放置阶段，GA-VMP将遗传算法应用到虚拟机的重新分配过程中形成了一个全新的虚拟机迁移模型。设计云数据中心的能量消耗数学模型，以能量消耗最小作为遗传算法的目标函数。Cloudsim模拟器仿真结果表明：在总体能量消耗、虚拟机迁移次数、服务等级协议违规率等指标上明显降低，平衡指标参数只有少量的增加。仿真结果可为其他企业构造节能云数据中心提供参考作用。

关键词: 智能计算, 物理主机, 虚拟机合并, 虚拟机分配, 遗传算法

Abstract: A genetic algorithm based approach for virtual machine placement called GA-VMP was proposed. In GA-VMP, local regression robust (LRR) algorithm was adopted to identify critical hosts in the physical host status detection procedure; minimum migration time (MMT) policy was also used for selecting VMs on critical hosts to be migrated. In the virtual placement, genetic algorithm was used to find a near-optimal solution and thus formed a new virtual machine migration model called LRR-MMT-GA. The mathematic model for energy consumption in GA-VMP was also designed and the minimized energy consumption was used as the objective function in genetic algorithm. The experimental results and performance analysis show our strategy leads to a further improvement in energy consumption and virtual machine migration. Our strategy is helpful for other cloud providers to build a low energy consumption cloud data center.

Key words: intelligent algorithm, physical host, virtual machine consolidation, virtual machine allocation, genetic algorithm

徐胜超. 利用遗传算法完成虚拟机放置策略的优化[J]. 计算机与现代化, 2020, 0(12): 25-31.

XU Sheng-chao. Using Genetic Algorithm for Virtual Machine Placement Optimization[J]. Computer and Modernization, 2020, 0(12): 25-31.

参考文献

［1］ SHI T, MA H, CHEN G. Energy-aware container consolidation based on PSO in cloud data centers［C］// 2018 IEEE Congress on Evolutionary Computation. IEEE, 2018:DOI:10.1109/CEC.2018.8477708.
［2］ AHAMED F, SHAHRESTANI S A, JAVADI B. Security aware and energy-efficient virtual machine consolidation in cloud computing systems［C］// 2016 IEEE Trustcom/BigDataSE/ISPA. IEEE, 2016:1516-1523.
［3］ MANN Z A. Resource optimization across the cloud stack［J］. IEEE Transactions on Parallel and Distributed Systems, 2018,29(1):169-182.
［4］ CALHEIROS R N, RANJAN R, BELOGLAZOV A, et al. CloudSim: A toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms［J］. Software：Practice & Experience, 2011,41(1):23-50.
［5］ BELOGLAZOV A, BUYYA R. Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers［J］. Concurrency and Computation:Practice and Experience, 2012,24(13):1397-1420.
［6］ GitHub. CloudSim: A Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services［EB/OL］. ［2020-06-22］. http://github.〖KG-*3〗com/Cloudslab/cloudsim/.〖HJ1.16mm〗
［7］ BELOGLAZOV A, ABAWAJY J, BUYYA R. Energy-aware resource allocation heuristics for efficient management of data centers for cloud computing［J］. Future Generation Computer Systems, 2012,28(5):755-768.
［8］〖KG-*4〗 FARAHNAKIAN F, LILJEBERG P, PLOSILA J. LiRCUP: Linear regression based CPU usage prediction algorithm for live migration of virtual machines in data centers［C］// Proceedings of the 2013 39th Euromicro Conference on Software Engineering and Advanced Applications. IEEE, 2013:357-364.
［9］ MAURYA K, SINHA R. Energy conscious dynamic provisioning of virtual machines using adaptive migration thresholds in cloud data center［J］. International Journal of Computer Science and Mobile Computing, 2013,2(3):74-82.
［10］ZHOU Z, HU Z G, LI K Q. Virtual machine placement algorithm for both energy-awareness and SLA violation reduction in cloud data centers［J］. Scientific Programming, 2016:DOI:10.1155/2016/5612039.
［11］KIM G, LEE W. Stable matching with ties for cloud-assisted smart TV services［C］// 2014 IEEE International Conference on Consumer Electronics (ICCE). 2014:558-559.
［12］XIONG A P, XU C X. Energy efficient multiresource allocation of virtual machine based on PSO in cloud data center［J］. Mathematical Problems in Engineering, 2014(6):1-8.
［13］ALBOANEEN D A, TIANFIELD H, ZHANG Y. Glowworm swarm optimisation algorithm for virtual machine placement in cloud computing［C］// 2016 IEEE Conferences on Ubiquitous Intelligence & Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People, and Smart World Congress. IEEE, 2016.
［14］BEIK R. Green cloud computing: Greedy algorithms for virtual machines migration and consolidation to optimize energy consumption in a data center［J］. International Journal of Digital Application & Contemporary Research, 2014,9(2):1-9.
［15］WANG J V, FOK K Y, CHENG C T, et al. A stable matching-based virtual machine allocation mechanism for cloud data centers［C］// 2016 IEEE World Congress on Services (SERVICES). 2016:103-106.
［16］LUO J P， LI X， CHEN M R. Hybrid shuffled frog leaping algorithm for energy-efficient dynamic consolidation of virtual machines in cloud data centers［J］. Expert Systems with Applications, 2014,41(13):5804-5816.
［17］VASUDEVAN M, TIAN Y C, TANG M L, et al. Energy-efficient application assignment in profile-based data center management through a repairing genetic algorithm［J］. Applied Soft Computing, 2018,67:399-408.
［18］FARAHNAKIAN F, ASHRAF A, PAHIKKALA T, et al. Using ant colony system to consolidate VMs for green cloud computing［J］. IEEE Transactions on Services Computing, 2015,8(2):187-198.

［19］JOSEPH C T, CHANDRASEKARAN K, CYRIAC R. A novel family genetic approach for virtual machine allocation［J］. Procedia Computer Science, 2015,46:558-565.

［20］DUGGAN M, FLESK K, DUGGAN J, et al. A reinforcement learning approach for dynamic selection of virtual machines in cloud data centres［C］// The 6th International Conference on Innovative Computing Technology (INTECH). IEEE, 2016:92-97.
［21］USMAN M J, ISMAIL A S, CHIZARI H, et al. Energy-efficient virtual machine allocation technique using flower pollination algorithm in cloud datacenter: A panacea to green computing［J］. Journal of Bionic Engineering, 2019,16(2):354-366.
［22］LIU X F, ZHAN Z H, DENG J D, et al. An energy efficient ant colony system for virtual machine placement in cloud computing［J］. IEEE Transactions on Evolutionary Computation, 2018,22(1):113-128.
［23］FAN X B, WEBER W D, BARROSO L A. Power provisioning for a warehouse-sized computer［C］// Proceedings of the 34th Annual International Symposium on Computer Architecture. 2007:13-23.
［24］SPEC. Benchmarks, Standard Performance Evaluation Corporation［EB/OL］. ［2020-06-22］. https://www.spec.org/.
［25］WANG J V, CHENG C T, TSE C K. Effects of correlation-based VM allocation criteria to cloud data centers［C］// 2016 International Conference on Cyber-enabled Distributed Computing & Knowledge Discovery. IEEE, 2016:398-401.

[1]	王佳1, 顾文俊1, 鞠炜刚2, 李玉维1, 张云龙2, 米传民3, 周志鹏3. 基于多元级差优良化遗传算法的环境拓扑结构任务调度[J]. 计算机与现代化, 2024, 0(10): 65-73.
[2]	王志强, 郑爽. 基于半监督学习的StyleGAN图像生成模型[J]. 计算机与现代化, 2024, 0(06): 14-18.
[3]	王东岳, 刘浩. 基于多智能体遗传算法的云平台抗虚假数据注入攻击方法 #br# #br#[J]. 计算机与现代化, 2024, 0(04): 21-26.
[4]	林启钊, 彭志平, 郭棉, 崔得龙. 基于双向多步预测的炉管温度场重构方法[J]. 计算机与现代化, 2024, 0(01): 53-58.
[5]	刘显茁, 邓韦斯, 谢恩彦. 考虑分布式发电并网的配电网自适应保护系统[J]. 计算机与现代化, 2023, 0(09): 120-126.
[6]	张志霞, 谢宝强. 基于FCGA-LSTM与迁移学习的天然气负荷预测[J]. 计算机与现代化, 2023, 0(07): 7-12.
[7]	汪敏, 徐英豪, 朱习军. 基于改进遗传算法优化BP神经网络的糖尿病并发症预测模型#br#[J]. 计算机与现代化, 2022, 0(11): 69-74.
[8]	王扬, 陈梅, 李晖. FOCoR:一种基于特征选择优化的课程推荐技术[J]. 计算机与现代化, 2022, 0(10): 1-7.
[9]	冉昊杰, 王宏智. 基于改进模拟退火算法的生鲜农产品配送中心选址[J]. 计算机与现代化, 2022, 0(10): 36-40.
[10]	战俊伟, 庄毅. 基于能耗与延迟优化的移动边缘计算任务卸载模型及算法[J]. 计算机与现代化, 2022, 0(08): 86-93.
[11]	吴代漾, 赵洁, 梁家铭, 董振宁, 梁周扬. C2C在线短租跨平台房东匹配算法[J]. 计算机与现代化, 2022, 0(06): 43-48.
[12]	徐胜超, 熊茂华. 基于遗传算法的容器云资源配置优化[J]. 计算机与现代化, 2022, 0(01): 108-112.
[13]	徐明, 张剑铭, 陈松航, 陈豪. 柔性作业车间调度问题的多目标优化算法[J]. 计算机与现代化, 2021, 0(12): 1-6.
[14]	张林鹏, 汪西原, 李强. 基于双池化特征加权结构CNN的图像分类[J]. 计算机与现代化, 2021, 0(11): 67-71.
[15]	陈春燕, 刘梦赤. 基于粒子群遗传算法的智能组卷策略[J]. 计算机与现代化, 2021, 0(08): 16-23.