A Multi-Objective Optimization Method of Initial Virtual Machine Fault-Tolerant Placement for Star Topological Data Centers of Cloud Systems
A Multi-Objective Optimization Method of Initial Virtual Machine Fault-Tolerant Placement for Star Topological Data Centers of Cloud Systems
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DOI：
10.26599/tst.2019.9010044
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发表时间：
2021-02-01
影响因子：
6.6
通讯作者：
Jiang, Jianhui
中科院分区：
文献类型：
--
作者：
Zhang, Wei;Chen, Xiao;Jiang, Jianhui
虚拟化技术是云计算系统统一资源层最重要的技术。虚拟机的管理方法有静态放置和动态管理两种。虚拟机的动态管理是基于虚拟机初始布局结构的，而虚拟机初始布局结构将影响虚拟机动态管理的效率。当VM发生故障时，如果不考虑容错，则部署在故障VM上的云应用程序将崩溃。基于服务级别协议违反率、资源剩余率、功耗率、故障率和容错成本等多个因素，建立了云系统星星拓扑数据中心初始虚拟机容错布局模型。然后，提出了一种启发式蚁群算法求解模型。服务提供虚拟机的配置采用蚁群算法，冗余虚拟机的配置采用传统的启发式算法。仿真实验、真实的集群实验和故障注入实验结果表明，与传统的最佳拟合下降法和首次拟合下降法相比，该方法能获得更好的虚拟机容错布局方案。
Virtualization is the most important technology in the unified resource layer of cloud computing systems. Static placement and dynamic management are two types of Virtual Machine (VM) management methods. VM dynamic management is based on the structure of the initial VM placement, and this initial structure will affect the efficiency of VM dynamic management. When a VM fails, cloud applications deployed on the faulty VM will crash if fault tolerance is not considered. In this study, a model of initial VM fault-tolerant placement for star topological data centers of cloud systems is built on the basis of multiple factors, including the service-level agreement violation rate, resource remaining rate, power consumption rate, failure rate, and fault tolerance cost. Then, a heuristic ant colony algorithm is proposed to solve the model. The service-providing VMs are placed by the ant colony algorithms, and the redundant VMs are placed by the conventional heuristic algorithms. The experimental results obtained from the simulation, real cluster, and fault injection experiments show that the proposed method can achieve better VM fault-tolerant placement solution than that of the traditional first fit or best fit descending method.