Modeling Energy Consumption of Virtual Machines in DVFS-Enabled Cloud Data Centers
Jianzhou Mao, Tathagata Bhattacharya, Xiaopu Peng, Ting Cao, Xiao Qin · 2020
To cut back energy consumption of virtual-machine-powered data centers, we build an optimization model for virtual machines running in DVFS-enabled cloud data centers. With the model in place, cloud computing systems are equipped to keep track of dynamic power and static power of processors in virtual machines. Unlike existing dynamic voltage and frequency scaling schemes, our solution orchestrates frequency requirements rather than task execution times. The model makes it possible to obtain an optimal frequency ratio, which minimizes energy consumption of virtual machines. As a result, a data center's energy efficiency is boosted by controlling CPU frequency to meet the optimal frequency ratio. We demonstrate a way of manipulating frequency ratios to pushing up energy efficiency without violating virtual machines' frequency requirements. The experimental results unveil that our modeling approach offers a practical way of conserving the energy consumption of virtual machines running in data centers.