Abstract: In cloud computing environment, Virtual Machine (VM) dynamic migration algorithm with static threshold could not optimize the energy consumption and load balancing with real-time cloud workload. In response to these problems, the proposed work defined the migration threshold adaptively using the statistical regularity of workload historical data, in order to achieve the adaptive energy-efficient VM scheduling algorithm in cloud computing environment. Dynamic voltage frequency scaling (DVFS) was adopted as the static energy saving strategy, while VM live migrations with 2 AHOD (Adaptive Host Overloading Detection) algorithms were adopted as the dynamic energy saving strategy in cloud data center. After the simulation and contrast to the static energy saving of DVFS and the dynamic energy saving with AHOD algorithms, the results show that:DVFS and AHOD strategy can significantly save energy 68% or more, although VM dynamic migration of AHOD strategy caused SLAV (Service the Level Agreement Violation), which lead to the degradation of QoS (Quality of Service) and the overall performance of the data center in cloud computing. However reasonable modeling is able to balance the energy consumption and QoS, security threshold of 1.2 in AHOD algorithm could consume the energy efficiently, achieve the adaptive load balancing of virtual machines migration dynamically.