基于双适应度遗传算法的虚拟机放置的研究

减少数据中心产生的网络时延以及优化数据中心能源消耗和物理资源的浪费等越来越受到研究者的关注。主要关注数据中心的物理资源的浪费和数据中心产生的网络时延,并且建模一个多目标优化问题:最小化数据中心的物理资源以及数据中心的时延。通过改进型双适应度遗传算法将两个目标同时优化,将其结果与贪心算法进行比较,实验结果表明,此算法优于贪心算法,是云环境下有效的虚拟机放置算法。

Virtual Machine Placement Algorithm Based on Improved Genetic Algorithm

Reducing the network delay and optimizing energy consumption and resource waste in the data centers have become increasingly important in the world.This paper focused on the resource waste and the network delay in the data centers and modeled the virtual machine placement to solve multi-objective optimization problems,such as minimizing physical machine resources and minimizing total network delay.Through the double-fitness genetic algorithm(CGA),we optimized the two objects at the same time.There is a contrast between CGA and FFD through simulation experiment,and the result is that CGA is better and it is an efficient virtual machine placement algorithm in the cloud environment.