1. School of Software, Xinjiang University, Urumqi Xinjaing 830008, China 2. 69031 Troops, Urumqi Xinjiang 830042, China 3. School of Information Science and Engineering, Xinjiang University, Urumqi Xinjiang 830046, China 4. School of Software, Xinjiang University, Urumqi Xinjiang 830008, China
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.
2013, Vol. 33 
