云数据中心高效的虚拟机整合方法

doi:10.11772/j.issn.1001-9081.2017061588

计算机应用 ›› 2018, Vol. 38 ›› Issue (2): 550-556.DOI: 10.11772/j.issn.1001-9081.2017061588

云数据中心高效的虚拟机整合方法

喻新荣¹, 李志华^1,2, 闫成雨¹, 李双俐¹

1. 江南大学物联网工程学院, 江苏无锡 214122;
2. 物联网应用技术教育部工程研究中心(江南大学), 江苏无锡 214122

收稿日期:2017-06-27 修回日期:2017-08-22 出版日期:2018-02-10 发布日期:2018-02-10
通讯作者: 李志华
作者简介:喻新荣(1992-),男,江苏南通人,硕士研究生,CCF会员,主要研究方向:云计算、并行计算、分布式计算;李志华(1969-),男,湖南保靖人,副教授,博士,主要研究方向:网络技术、信息安全、数据挖掘;闫成雨(1992-),男,江苏徐州人,硕士,主要研究方向:云计算、网格与分布式计算;李双俐(1992-),女,河南新乡人,硕士研究生,主要研究方向:云计算、分布式计算。
基金资助:
江苏省产学研联合创新基金资助项目（BY2013015-23）。

High efficient virtual machines consolidation method in cloud data center

YU Xinrong¹, LI Zhihua^1,2, YAN Chengyu¹, LI Shuangli¹

1. School of IoT Engineering, Jiangnan University, Wuxi Jiangsu 214122, China;
2. Engineering Research Center of IoT Technology Application of Ministry of Education(Jiangnan University), Wuxi Jiangsu 214122, China

Received:2017-06-27 Revised:2017-08-22 Online:2018-02-10 Published:2018-02-10
Supported by:
This work is partially supported by the Production-Study-Research Joint Innovation Foundation of Jiangsu Province (BY2013015-23).

摘要/Abstract

摘要： 针对传统虚拟机整合（VMC）方法难以保持主机工作负载长期稳定的问题，提出一种基于高斯混合模型的高效虚拟机整合（GMM-VMC）方法。为了准确地预测主机负载的变化趋势，首先，使用高斯混合模型（GMM）对活动物理主机的工作负载历史记录进行拟合；然后，根据活动物理主机工作负载的GMM和主机自身的资源配置情况计算主机的过载概率，并根据过载概率判定主机是否存在过载风险；对存在过载风险的物理主机，根据部署在该物理主机上的虚拟机对降低主机过载风险的贡献和虚拟机迁移所需的时间这两个指标进行待迁移虚拟机选择；最后，使用GMM估算待迁移虚拟机对各个目标主机过载风险的影响，并选择受影响最小的主机作为目标主机。通过CloudSim仿真平台模拟该GMM-VMC方法，并根据能源消耗、服务质量（QoS）、整合效率等指标与已有的整合方法进行对比，实验结果表明，GMM-VMC方法能够有效地降低数据中心能耗，提高服务质量。

关键词: 云计算, 虚拟机整合, 高斯混合模型, 主机过载概率, 服务质量

Abstract: Concerning the problem that the workload of hosts in data center cannot maintain long-term stability by executing traditional Virtual Machine Consolidation (VMC), a high efficient Gaussian Mixture Model-based VMC (GMM-VMC) method was proposed. Firstly, to accurately predict the variation trend of workload in hosts, Gaussian Mixture Model (GMM) was used to fit the workload history of hosts. Then, the overload probability of a host was calculated according to the GMM of its workload and resource capacity. Next, the aforementioned overload probability was taken as the criteria to determine whether the host is overloaded or not. Besides, some virtual machines hosted by overloaded hosts which can significantly degrade overload risk and demand less migration time were selected to migrate. At last, these migrated virtual machines were placed in new hosts which have less effect on workload variation after placement estimated by GMM. Using CloudSim toolkit, GMM-VMC method was validated and compared with other methods on energy consumption, Quality of Service (QoS) and efficiency of consolidation. The experimental results show that the GMM-VMC method can degrade energy consumption in data center and improve QoS.

Key words: cloud computing, Virtual Machine Consolidation (VMC), Gaussian Mixture Model (GMM), overload probability of host, Quality of Service (QoS)

中图分类号:

TP393.027

喻新荣, 李志华, 闫成雨, 李双俐. 云数据中心高效的虚拟机整合方法[J]. 计算机应用, 2018, 38(2): 550-556.

YU Xinrong, LI Zhihua, YAN Chengyu, LI Shuangli. High efficient virtual machines consolidation method in cloud data center[J]. Journal of Computer Applications, 2018, 38(2): 550-556.

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云数据中心高效的虚拟机整合方法

High efficient virtual machines consolidation method in cloud data center

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