基于多目标优化的虚拟机放置方法

在虚拟机放置问题中，传统启发式方法不能完全适用于复杂的云计算环境，采用智能算法的研究又缺乏对时间开销的考虑。针对上述问题，提出一种基于Memetic算法的虚拟机放置(Memetic algorithm-based virtual machine placement MAVMP)方法。MAVMP方法针对云数据中心运营情况建立了最小化能耗、最小化运行时服务等级协议违例率(service level agreement violation time per active host， SLATAH)以及最大化资源利用率的多目标优化模型，将虚拟机按照资源请求情况进行分类，并利用该分类方法改进了Memetic算法，利用改进后的Memetic算法求解多目标优化模型，得到虚拟机放置方案。仿真实验结果表明，仿真数据中心利用MAVMP方法进行虚拟机放置后，其在能耗、资源利用率以及服务质量的评价指标上都有着良好表现。并且，MAVMP方法与已有的基于智能算法的虚拟机放置方法相比计算时间也大幅下降。

Virtual machine placement method based on multi-objective optimization

In the virtual machine placement problem, the traditional heuristic methods are not entirely applicable to the complex cloud computing environment, and the researches using intelligent algorithms lack the consideration of time overhead. To solve the above problems, a Memetic algorithm-based virtual machine placement (MAVMP) method is proposed. Firstly, The MAVMP method establishes a multi-objective optimization model for minimizing energy consumption, minimizing the service-level agreement violation times per active host (SLATAH) and maximizing resource utilization according to the operation situation of cloud data centers. Secondly, on the basis of resource requests, virtual machines are classified, improving the Memetic algorithm. Finally, the improved Memetic algorithm is used to solve the multi-objective optimization model, and then obtain the virtual machine placement plan. The results of simulation test show that the simulation data center using the MAVMP method to place virtual machines has good performances in energy consumption,resource utilization and service quality. Moreover, in contrast to the existing intelligent algorithm-based virtual machine placement method, the calculation time of the MAVMP method decreases sharply.