云环境中多目标优化的虚拟机放置算法

虚拟机放置（VMP）是虚拟机整合的核心，是一个多资源约束的多目标优化问题。高效的VMP算法不仅能显著地降低云数据中心能耗、提高资源利用率，还能保证服务质量（QoS）。针对数据中心能耗高和资源利用率低的问题，提出了基于离散蝙蝠算法的虚拟机放置（DBA-VMP）算法。首先，把最小化能耗和最大化资源利用率作为优化目标，建立多目标约束的VMP优化模型；然后，通过效仿人工蚁群在觅食过程中共享信息素的机制，将信息素反馈机制引入蝙蝠算法，并对经典蝙蝠算法进行离散化改进；最后，用改进的离散蝙蝠算法求解模型的Pareto最优解。实验结果表明，与其他多目标优化的VMP算法相比，所提算法在使用不同数据集的情况下都能有效降低能耗，提高资源利用率，实现了在保证QoS的前提下的降低能耗和提高资源利用率两者之间的优化平衡。

Multi-objective optimization algorithm for virtual machine placement under cloud environment

Virtual Machine Placement (VMP) is the core of virtual machine consolidation and is a multi-objective optimization problem with multiple resource constraints. Efficient VMP algorithm can significantly reduce energy consumption, improve resource utilization, and guarantee Quality of Service (QoS). Concerning the problems of high energy consumption and low resource utilization in data center, a Discrete Bat Algorithm-based Virtual Machine Placement (DBA-VMP) algorithm was proposed. Firstly, an optimization model with multi-object constraints was established for VMP, with minimum energy consumption and maximum resource utilization as optimization objectives. Then, the pheromone feedback mechanism was introduced in the bat algorithm by emulating the pheromone sharing mechanism of artificial ant colonies in the foraging process, and the bat algorithm was improved and discretized. Finally, the improved discrete bat algorithm was used to solve the Pareto optimal solutions of the model. The experimental results show that compared with other multi-objective optimization algorithms for VMP, the proposed algorithm can effectively reduce energy consumption and improve resource utilization, and achieves an optimal balance between reducing energy consumption and improving resource utilization under the premise of guaranteeing QoS.