Research on virtual machine consolidation strategy based on combined prediction and energy-aware in cloud computing platform

Abstract In the era of information explosion, energy consumption cloud data centers is significant. It’s critical to reduce large-scale while guaranteeing quality service (QoS), especially video computing platforms. The application virtual machine (VM) consolidation has been regarded as a promising approach improve resource utilization and save centers. this paper, an efficient QoS-aware VM method proposed address issues. A combined prediction model based on grey ARIMA applied host status detection, we provide new scheme that placement policy varying search most suitable selection called AUMT selecting with low average CPU migration time. Extensive experimental results cloudsim simulator demonstrate enables achieve objectives reducing consumption, number migrations, SLAV ESV by 56.07%, 79.21%, 91.01% 84.34% compared benchmark methods can migrations 15.46%, 28.11% 3.96% state-of-the-art method.