MECC: A Mobile Edge Collaborative Caching Framework Empowered by Deep Reinforcement Learning

IEEE Network ◽  
2021 ◽  
Vol 35 (4) ◽  
pp. 176-183
Author(s):  
Siya Xu ◽  
Xin Liu ◽  
Shaoyong Guo ◽  
Xuesong Qiu ◽  
Luoming Meng
Keyword(s):  
Reinforcement Learning ◽  
Collaborative Caching

scholarly journals Deep Reinforcement Learning-Based Collaborative Video Caching and Transcoding in Clustered and Intelligent Edge B5G Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-16
Author(s):  
Zheng Wan ◽  
Yan Li
Keyword(s):  
Reinforcement Learning ◽  
Video Streaming ◽  
User Preference ◽  
User Requirement ◽  
State Spaces ◽  
Video Caching ◽  
Network Condition ◽  
Collaborative Caching ◽  
Streaming Services ◽  
Streaming Service

In the next-generation wireless communications system of Beyond 5G networks, video streaming services have held a surprising proportion of the whole network traffic. Furthermore, the user preference and demand towards a specific video might be different because of the heterogeneity of users’ processing capabilities and the variation of network condition. Thus, it is a complicated decision problem with high-dimensional state spaces to choose appropriate quality videos according to users’ actual network condition. To address this issue, in this paper, a Content Distribution Network and Cluster-based Mobile Edge Computing framework has been proposed to enhance the ability of caching and computing and promote the collaboration among edge severs. Then, we develop a novel deep reinforcement learning-based framework to automatically obtain the intracluster collaborative caching and transcoding decisions, which are executed based on video popularity, user requirement prediction, and abilities of edge servers. Simulation results demonstrate that the quality of video streaming service can be significantly improved by using the designed deep reinforcement learning-based algorithm with less backhaul consumption and processing costs.

scholarly journals Deep Reinforcement Learning Empowered Edge Collaborative Caching Scheme for Internet of Vehicles

2022 ◽  
Vol 42 (1) ◽  
pp. 271-287
Author(s):  
Xin Liu ◽  
Siya Xu ◽  
Chao Yang ◽  
Zhili Wang ◽  
Hao Zhang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Internet Of Vehicles ◽  
Collaborative Caching ◽  
Caching Scheme

Bayes factors for reinforcement-learning models of the Iowa gambling task.

Decision ◽  
2016 ◽  
Vol 3 (2) ◽  
pp. 115-131 ◽  
Author(s):  
Helen Steingroever ◽  
Ruud Wetzels ◽  
Eric-Jan Wagenmakers
Keyword(s):  
Reinforcement Learning ◽  
Iowa Gambling Task ◽  
Bayes Factors ◽  
Gambling Task ◽  
Learning Models ◽  
Reinforcement Learning Models
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