云数据中心中存在着高能耗和高服务水平协议违约率的问题，为了解决此问题，提出了一种基于多目标优化的虚拟机整合算法. 综合考虑能耗、服务质量和迁移开销等多种因素，将虚拟机整合问题构建为一个具有资源约束的多目标优化问题. 使用蚁群系统算法对该多目标优化问题进行求解，进行虚拟机整合，获得近似最优的虚拟机主机映射关系. 为了减少算法复杂度，利用CPU利用率双阈值来判断主机负载状态，根据主机负载状态分阶段进行整合并使用不同的整合策略. 基于CloudSim平台对多目标优化的虚拟机整合算法和其他6种虚拟机整合算法进行仿真实验，将本文算法与现有虚拟机整合算法实验结果进行比较，结果表明本文提出的算法在能耗和服务水平协议违约方面优化显著，具有较好的综合性能.
There exist problems of high energy consumption and high Service Level Agreement (SLA) violation rates in cloud data centers，which urgently need to be resolved. In order to solve the above problems，a Multi-objective Virtual Machine Consolidation Algorithm (MOVMC) was proposed to reduce energy consumption and SLA violation. Taking into account multiple factors including energy consumption，service quality and migration overhead，the virtual machine consolidation problem was constructed as a resource-constrained multi-objective optimization problem. Ant colony system algorithm was employed to perform virtual machine consolidation and obtain the near-optimal mapping relation between virtual machines and hosts as the solution to the multi-objective optimization problem. In order to reduce the algorithm complexity，the double thresholds of CPU utilization were leveraged to judge the host load status and a multi-stage consolidation was performed according to the host load status，in which different consolidation strategies were used. Simulation experiments were conducted on CloudSim platform for MOVMC algorithm and six other virtual machine consolidation algorithms. The experimental results show that，compared with the existing virtual machine consolidation algorithm，the proposed algorithm has significant optimization in terms of energy consumption and SLA violation，and an excellent comprehensive performance.