A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing

2018 ◽  
Vol 13 (3) ◽  
pp. 85-98
Author(s):  
Wei Zhang ◽  
Huiling Shi ◽  
Xinming Lu ◽  
Longquan Zhou
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Data Distribution ◽  
Transmission Efficiency ◽  
Multimedia Data ◽  
Low Latency ◽  
Bandwidth Optimization ◽  
Multimedia Conference System ◽  
Multimedia Conference ◽  
Conference System

With the development of information technology, more and more people use multimedia conference system to communicate or work across regions. In this article, an ultra-reliable and low-latency solution based on Deep Learning and assisted by Cloud Computing for multimedia conference system, called UCCMCS, is designed and implemented. In UCCMCS, there are two-tiers in its data distribution structure which combines the advantages of cloud computing. And according to the requirements of ultra-reliability and low-latency, a bandwidth optimization model is proposed to improve the transmission efficiency of multimedia data so as to reduce the delay of the system. In order to improve the reliability of data distribution, the help of cloud computing node is used to carry out the retransmission of lost data. the experimental results show UCCMCS could improve the reliability and reduce the latency of the multimedia data distribution in multimedia conference system.

A Deep Learning Solution for Multimedia Conference System Assisted by Cloud Computing

2020 ◽  
pp. 372-385
Author(s):  
Wei Zhang ◽  
Huiling Shi ◽  
Xinming Lu ◽  
Longquan Zhou
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Data Distribution ◽  
Transmission Efficiency ◽  
Multimedia Data ◽  
Low Latency ◽  
Computing Node ◽  
Multimedia Conference System ◽  
Multimedia Conference ◽  
Conference System

With the development of information technology, more and more people use multimedia conference system to communicate or work across regions. In this article, an ultra-reliable and low-latency solution based on Deep Learning and assisted by Cloud Computing for multimedia conference system, called UCCMCS, is designed and implemented. In UCCMCS, there are two-tiers in its data distribution structure which combines the advantages of cloud computing. And according to the requirements of ultra-reliability and low-latency, a bandwidth optimization model is proposed to improve the transmission efficiency of multimedia data so as to reduce the delay of the system. In order to improve the reliability of data distribution, the help of cloud computing node is used to carry out the retransmission of lost data. the experimental results show UCCMCS could improve the reliability and reduce the latency of the multimedia data distribution in multimedia conference system.

scholarly journals Deep Learning Entrusted to Fog Nodes (DLEFN) Based Smart Agriculture

2020 ◽  
Vol 10 (4) ◽  
pp. 1544 ◽  
Author(s):  
Kyuchang Lee ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Cloud Computing ◽  
Deep Learning ◽  
Fog Computing ◽  
Multimedia Data ◽  
Network Congestion ◽  
Service Delay ◽  
Comparison Results ◽  
Smart Agriculture ◽  
Iot Devices ◽  
Computing Capacity

Colossal amounts of unstructured multimedia data are generated in the modern Internet of Things (IoT) environment. Nowadays, deep learning (DL) techniques are utilized to extract useful information from the data that are generated constantly. Nevertheless, integrating DL methods with IoT devices is a challenging issue due to their restricted computational capacity. Although cloud computing solves this issue, it has some problems such as service delay and network congestion. Hence, fog computing has emerged as a breakthrough way to solve the problems of using cloud computing. In this article, we propose a strategy that assigns a portion of the DL layers to fog nodes in a fog-computing-based smart agriculture environment. The proposed deep learning entrusted to fog nodes (DLEFN) algorithm decides the optimal layers of DL model to execute on each fog node, considering their available computing capacity and bandwidth. The DLEFN individually calculates the optimal layers for each fog node with dissimilar computational capacities and bandwidth. In a similar experimental environment, comparison results clearly showed that proposed method accommodated more DL application than other existing assignment methods and utilized resources efficiently while reducing network congestion and processing burden on the cloud.

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