Energy saving and load balance strategy in cloud computing
Qian Yuron · Journal of Computer Applications · 2013
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.