<p>In cloud data centers, excessive or insufficient resource utilization of physical machines(PMs) can have adverse effects. Resource utilization should be controlled reasonably to achieve an optimal balance among energy consumption, resource waste rate, and quality of service(QoS). To address this issue, the virtual machine placement problem is abstracted as a multi-objective optimization problem, with the optimization objective of minimizing the energy consumption of cloud data centers, resource waste rate, and probability of host overload. A novel multi-objective flower pollination algorithm based on decomposition(MOFPA/D) is proposed by applying a discrete approach to the flower pollination algorithm (FPA) and then integrating with the well-established multi-objective evolutionary algorithm based on decomposition optimization framework(MOEA/D). Subsequently, the aforementioned optimization problem is solved by using the proposed algorithm, which results in a globally optimal virtual machine placement algorithm. Moreover, the integration of this algorithm with the proposed host overload-detection algorithm, a virtual-machine-selection algorithm, and a low-load host-detection algorithm enables the development of a virtual machine consolidation method, named EUQ-VMC, which aims to achieve efficient resource utilization and service-quality perception. Simulation results show that the EUQ-VMC method significantly reduces energy consumption and enhances resource utilization and QoS compared with other methods.</p>