一种基于qemu的动态迁移模型

Analytical Model for Qemu-based Live Migration Strategy

在线迁移已经成为数据中心的一个核心管理工具,广泛用于负载平衡、服务器整合和系统维护等方面。精确地预测在线迁移性能是制定有效迁移决策的前提。在广泛用于开源云计算的qemu-kvm虚拟化平台中,迁移策略与传统的预拷贝策略存在差异,导致已有的迁移模型无法有效地应用于该平台。为此,提出一种基于qemu-kvm平台的迁移策略的建模方法,基于模型抽取影响在线迁移性能的关键因素,分析它们与迁移性能之间的数学关系,最后针对这些关键参数建立相应的测试环境,以此测试评估模型的正确性与精确性。测试结果表明模型预测迁移时间和迁移数据总量的精确度在95%以上。

Live migration is a powerful management tool in data center and has been widespreadly applied for virtual machine load balancing,fault tolerance,power management and other applications.Whether evaluation for performance of live migration of virtual machines is precise or not has directly influence on effects of live migration decisions.Therefore,we proposed an analytical model for qemu-based live migration of virtual machines.Based the model,we extracted key parameters that affect the performance of live migration,and analyzed mathematic relations between these parameters and performance of live migration.Finally,we built some experiments to evaluate and verify the correctness and precision of the analytical model by comparing experiential and analysis results.Our experiential results show that the model yields higher than 95% prediction accuracy in migration time and total transferred data.