Research on Optimization of Multi Data Center Communication Network Architecture Based on Deep Learning
With the rapid development of cloud computing,big data,and the Internet of Things technology,multi data center communication network architecture is facing severe challenges in supporting large-scale data transmission and high quality of service requirements.Traditional network architecture optimization methods are often limited by their fixed rules and limited predictive ability.This article proposes a deep learning based optimization method for multi data center communication network architecture.By constructing a deep learning model,accurate prediction and optimization of key indicators such as network traffic and transmission delay can be achieved.The experimental results show that the proposed method can effectively improve data transmission efficiency and service quality,providing stable and reliable network support for cloud computing and big data applications.
multiple data centerscommunication network architecturedeep learning
NETL
NSTL
万方数据
