检索结果-南通市图书馆

作者： Mo YANG Xiu-li HUANG Wei-wei MIU Peng-fei YU Wei LI Ru-xia YANG State Key Laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications Global Energy Interconnection Research Institute Co. Ltd Communication branch State Grid Jiangsu Electric Power Co. Ltd

Ubiquitous Power IoT is an important part of smart grid or energy internet. edge network is a key component of smart grid and future energy Internet. IoT contains various terminals, diverse access networks and equipment. The allocation of energy-efficient resources under QoS guarantee is an effective means to ensure the reliable operation of the network. Due to the heterogeneity of the services carried and in order to reduce business operation risks and impact on the power grid, it is necessary to provide highly reliable resource allocation method to the ubiquitous power IoT business for the energy Internet. edge network can reduce end-to-end latency and reduce backhaul link traffic. However, mobile edge networks moving the function of storage and computing down to the edge nodes of network, which makes resource management more complex. Moreover, the computing resources in the edge network is limited and the effect to the ecological environment and economic cost should also be considered. So how to allocate resources such as bandwidth and power more effectively while meeting the needs of users becomes an important issue. Even if the Deep Reinforcement Learning（DRL） algorithm has been used to a lot of the work which are correlated to edge networks, there lacks the applications for green resource allocation. In this paper, a mechanism based on Deep Reinforcement Learning（DRL） for the resource allocation problem is proposed oriented to edge networks. The mechanism aims at how to allocate resources more efficiently and energy-saving while satisfying the requirements of each user. The simulation results show that the energy efficiency value could be obtained when the algorithm converges and stabilizes. The Energy Efficiency（EE） of the proposed mechanism and the productiveness in satisfying user requirements and implementing green resource allocation are validated.

关键词： Deep Reinforcement Learning Green Resource Allocation mobile edge network

来源： cnki会议评论

在线全文

cnki会议

学校读者我要写书评

暂无评论

A DQN-Based Cache Strategy for mobile edge networks

引用

Computers, Materials & Continua 2022年第5期71卷 3277-3291页

作者： Siyuan Sun Junhua Zhou Jiuxing Wen Yifei Wei Xiaojun Wang School of Electronic Engineering Beijing University of Posts and TelecommunicationsBeijing100876China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System EngineeringBeijing100854China Ningbo Sunny Intelligent Technology Co. LTDYuyao315400China Dublin City University Dublin 9Ireland

The emerging mobile edge networks with content caching capability allows end users to receive information from adjacent edge servers directly instead of a centralized data warehouse,thus the network transmission delay and system throughput can be improved *** the duplicate content transmissions between edge network and remote cloud can be reduced,the appropriate caching strategy can also improve the system energy efficiency of mobile edge networks to a great *** paper focuses on how to improve the network energy efficiency and proposes an intelligent caching strategy according to the cached content distribution model for mobile edge networks based on promising deep reinforcement learning *** deep neural network(DNN)and Q-learning algorithm are combined to design a deep reinforcement learning framework named as the deep-Q neural network(DQN),in which the DNN is adopted to represent the approximation of action-state value function in the Q-learning *** parameters iteration strategies in the proposed DQN algorithm were improved through stochastic gradient descent method,so the DQN algorithm could converge to the optimal solution quickly,and the network performance of the content caching policy can be *** simulation results show that the proposed intelligent DQN-based content cache strategy with enough training steps could improve the energy efficiency of the mobile edge networks significantly.

关键词： mobile edge network edge caching energy efficiency

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

在线全文

在线全文

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：