In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the network.Software Defined Network(SDN)is the latest and widely used technology in the ...In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the network.Software Defined Network(SDN)is the latest and widely used technology in the future communication networks,which would provide smart routing that is visible uni-versally.The various features of routing are supported by the information centric network,which minimizes the congestion in the dataflow in a network and pro-vides the content awareness through its mined mastery.Due to the advantages of the information centric network,the concepts of the information-centric net-work has been used in the paper to enable an optimal routing in the software-defined networks.Although there are many advantages in the information-centric network,there are some disadvantages due to the non-static communication prop-erties,which affects the routing in SDN.In this regard,artificial intelligence meth-odology has been used in the proposed approach to solve these difficulties.A detailed analysis has been conducted to map the content awareness with deep learning and deep reinforcement learning with routing.The novel aligned internet investigation technique has been proposed to process the deep reinforcement learning.The performance evaluation of the proposed systems has been con-ducted among various existing approaches and results in optimal load balancing,usage of the bandwidth,and maximization in the throughput of the network.展开更多
Built on a multi-variable exploratory experiment on technology-enhanced learning,this study was anempirical inqui ry into the variable of learning content and its resources. Informed by the empirical find-ings and con...Built on a multi-variable exploratory experiment on technology-enhanced learning,this study was anempirical inqui ry into the variable of learning content and its resources. Informed by the empirical find-ings and conclusions as well as relevant theoretical underpinnings,the article analyzed the m ode of rep-resentation of S-DIP deep learning resources, its c0rresponding representation methods and characteris-tics,and five development principles for deep learning resources.The analysis led to the construction ofa development model for CRF deep learning resources.The article was rounded off with a profound in-terpretation of the concept of deep learning resources and recommendation of application strategies.展开更多
文摘In a non-static information exchange network,routing is an overly com-plex task to perform,which has to satisfy all the needs of the network.Software Defined Network(SDN)is the latest and widely used technology in the future communication networks,which would provide smart routing that is visible uni-versally.The various features of routing are supported by the information centric network,which minimizes the congestion in the dataflow in a network and pro-vides the content awareness through its mined mastery.Due to the advantages of the information centric network,the concepts of the information-centric net-work has been used in the paper to enable an optimal routing in the software-defined networks.Although there are many advantages in the information-centric network,there are some disadvantages due to the non-static communication prop-erties,which affects the routing in SDN.In this regard,artificial intelligence meth-odology has been used in the proposed approach to solve these difficulties.A detailed analysis has been conducted to map the content awareness with deep learning and deep reinforcement learning with routing.The novel aligned internet investigation technique has been proposed to process the deep reinforcement learning.The performance evaluation of the proposed systems has been con-ducted among various existing approaches and results in optimal load balancing,usage of the bandwidth,and maximization in the throughput of the network.
文摘Built on a multi-variable exploratory experiment on technology-enhanced learning,this study was anempirical inqui ry into the variable of learning content and its resources. Informed by the empirical find-ings and conclusions as well as relevant theoretical underpinnings,the article analyzed the m ode of rep-resentation of S-DIP deep learning resources, its c0rresponding representation methods and characteris-tics,and five development principles for deep learning resources.The analysis led to the construction ofa development model for CRF deep learning resources.The article was rounded off with a profound in-terpretation of the concept of deep learning resources and recommendation of application strategies.
