Abstract: An adaptive Virtual Machine (VM) dynamic migration strategy of soft energy-saving was put forward to optimize energy consumption and load balance in cloud computing. The energy-saving strategy adopted Dynamic Voltage Frequency Scaling (DVFS) as the static energy-aware technology to achieve the sub-optimized static energy saving, and used online VM migration to achieve an adaptive dynamic soft energy-saving in cloud platform. The two energy-saving strategies were simulated and compared with each other in CloudSim platform, and the data were tested on PlanetLab platform. The results show that: Firstly, the adaptive soft and hard combination strategy in energy-saving can significantly save 96% energy; secondly, DVFS+MAD_MMT strategy using Median Absolute Deviation (MAD) to determine whether the host is overload, and choosing VM to remove based on Minimum Migration Time (MMT), which can save energy about 87.15% with low-load in PlanetLab Cloudlets than that of experimental environment; finally, security threshold of 2.5 in MAD_MMT algorithm can consume the energy efficiently and achieve the adaptive load balancing of virtual machines migration dynamically.

Key words: Dynamic Voltage Frequency Scaling (DVFS), Virtual Machine (VM) migration, CloudSim simulation, soft energy-saving, Minimum Migration Time (MMT)

摘要： 针对云计算服务环境下软硬件节能和负载均衡优化问题，提出一种自适应的云计算环境下虚拟机(VM)动态迁移软节能策略。该策略采用常用的硬件能耗感知技术——动态电压频率调节(DVFS)来实现分段优化的系统部件静态节能，又通过VM在线迁移技术实现云平台的动态自适应软件节能。在CloudSim云仿真平台下对比实现DVFS静态节能和自适应负载均衡的软节能策略，经PlanetLab云平台监测数据验证，结果表明：软硬结合的自适应能耗感知策略能够高效节能96%； DVFS+MAD_MMT节能策略(采用平均绝对偏差算法判定主机是否超载，基于最短迁移时间(MMT)原则选择VM移出)

关键词: 动态电压频率调节, 虚拟机迁移, CloudSim仿真, 软节能, 最短迁移时间