.png){kind=icon}
返回
A reinforcement learning optimization for future smart cities using software defined networking
.png){kind=icon}
PRE
AI
DOI：10.1007/s13042-020-01245-w.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Nowadays smart cities towards software defined network (SDN) approach will become better flexibility and manageability. A stronger, more dynamic network is an SDN network, which is precisely what a smart city network must be if it wants to be viable on a real-world scale. SDN architecture is developed to implement a learning framework for network optimization. The proposed method is called mixed-integer and reinforcement learned network optimization (MI-RLNO) for SDN monitoring. In the first phase, mixed-integer programming formulation is used as an optimization formulation for latency and convergence time. In the second phase, a reinforced Q Learning model is designed that uses communication and computation time as input state vector. Optimization formulation is used as the actions and strategies to be followed during the design and operation of communication networks, therefore contributing fairness and throughput. Simulation results improved the efficiency of the MI-RLNO method.
Keyword:
Software defined network
Mixed integer
Reinforcement
Q learning
Network optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
2.7
论文数:
3.2K
被引数:
5.6K
机构
引用论文
Varna-based optimization: a novel method for capacitated controller placement problem in SDN基于Varna的优化: 一种解决SDN中容能控制器放置问题的新方法
PREAI
分享收藏
Rotational Piezoelectric Energy Harvesting: A Comprehensive Review on Excitation Elements, Designs, and Performances.png)
Energies
OAAI
EnergiesIF0
OAAI分享收藏
查看更多内容