应用于软件定义网络中的机器学习算法分析

Analysis of Machine Learning Algorithms Applied in Software-Defined Networks

近年来随着当前互联网技术和移动通信技术的快速发展,网络系统中的基础设施、资源以及结构变得越来越复杂。为了更有效地组织、管理、维护和优化网络系统,需要部署更多的智能化设备。然而,由于传统网络固有的分布式特征,机器学习技术难以应用和部署在网络控制中,软件定义网络(SDN)技术的出现为机器学习技术应用于网络管理提供新的机遇与挑战。总结机器学习技术应用于SDN网络架构中的相关文献资料,并探讨所要面临的挑战。

In recent years, with the rapid development of current Internet technologies and mobile communication technologies, the infrastructure, resources, and structures in network systems have become more and more complex. In order to organize, manage, maintain and optimize network systems more effectively, more intelligent devices need to be deployed. However, due to the inherent distributed characteristics of traditional networks, machine learning technology is difficult to deploy in network control. The emergence of Software Defined Network (SDN) technology provides new machines and challenges for machine learning technology to be applied to network management. Summarizes the relevant literature on applying machine learning technology to the SDN network architecture and discusses the challenges to be faced.