基于机器学习的虚拟设备选择策略的设计

Design of Virtual Device Selection Strategy Based on Machine Learning

为了解决虚拟设备资源利用率低,能源消耗高等问题,设计了基于机器学习的虚拟设备决策算法.首先利用机器学习提出虚拟设备决策的优化算法,将机器学习与传统虚拟设备决策算法结合提出了基于机器学习的虚拟设备决策算法优化,解决了设备受资源限制的问题.然后利用机器学习技术中的DQN算法完成对虚拟设备决策算法的实现,通过系统自主学习,分析用户需求及软硬件环境,提出多种虚拟设备配置方案,最后经过系统对比选择出符合用户需求,同时资源消耗最优的虚拟设备配置方案.

In order to solve the problems of low resource utilization and high energy consumption of virtual equipment,a decision-making algorithm of virtual equipment based on machine learning is designed.Firstly,an optimization algorithm for virtual equipment decision-making is proposed based on machine learning,which combines machine learning with traditional virtual equipment decision-making algorithm and solves the problem of equipment resource constraints.Then,the DQN algorithm in machine learning technology is used to implement the decision-making algorithm of virtual equipment.Through the system autonomous learning,the user needs software and hardware environment are analyzed,and a variety of virtual equipment configuration schemes are proposed.Finally,through the system comparison,the virtual equipment configuration scheme which meets the user needs and consumes optimal resources is selected.