Integration of communication and computing in blockchain-enabled multi-access edge computing systems

2021 ◽  
Vol 18 (12) ◽  
pp. 297-314
Zhonghua Zhang ◽  
Jie Feng ◽  
Qingqi Pei ◽  
Le Wang ◽  
Lichuan Ma
IEEE Access ◽  
2021 ◽  
pp. 1-1
Chit Wutyee Zaw ◽  
Shashi Raj Pandey ◽  
Kitae Kim ◽  
Choong Seon Hong

2020 ◽  
Vol 19 (12) ◽  
pp. 7825-7835
Hao Gao ◽  
Wuchen Li ◽  
Reginald A. Banez ◽  
Zhu Han ◽  
H. Vincent Poor

Pavlos Athanasios Apostolopoulos ◽  
Georgios Fragkos ◽  
Eirini Eleni Tsiropoulou ◽  
Symeon Papavassiliou

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 343
Chunyang Hu ◽  
Jingchen Li ◽  
Haobin Shi ◽  
Bin Ning ◽  
Qiong Gu

Using reinforcement learning technologies to learn offloading strategies for multi-access edge computing systems has been developed by researchers. However, large-scale systems are unsuitable for reinforcement learning, due to their huge state spaces and offloading behaviors. For this reason, this work introduces the centralized training and decentralized execution mechanism, designing a decentralized reinforcement learning model for multi-access edge computing systems. Considering a cloud server and several edge servers, we separate the training and execution in the reinforcement learning model. The execution happens in edge devices of the system, and edge servers need no communication. Conversely, the training process occurs at the cloud device, which causes a lower transmission latency. The developed method uses a deep deterministic policy gradient algorithm to optimize offloading strategies. The simulated experiment shows that our method can learn the offloading strategy for each edge device efficiently.

2020 ◽  
Vol 14 (19) ◽  
pp. 3404-3409
Chuyen T. Nguyen ◽  
Quoc-Viet Pham ◽  
Huong-Giang T. Pham ◽  
Nhu-Ngoc Dao ◽  
Won-Joo Hwang

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