Deep Reinforcement Learning for Task Allocation in UAV-enabled Mobile Edge Computing

2021 ◽  
pp. 225-232
Author(s):  
Changliang Yu ◽  
Wei Du ◽  
Fan Ren ◽  
Nan Zhao
Keyword(s):  
Reinforcement Learning ◽  
Task Allocation ◽  
Edge Computing ◽  
Mobile Edge Computing

SO‐VMEC: Service offloading in virtual mobile edge computing using deep reinforcement learning

2021 ◽  
Author(s):  
Mohammed Laroui ◽  
Hatem Ibn‐Khedher ◽  
Moussa Ali Cherif ◽  
Hassine Moungla ◽  
Hossam Afifi ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Edge Computing

Task assignment in mobile edge computing networks: a deep reinforcement learning approach

2021 ◽  
Author(s):  
Mingjie Feng ◽  
Qi Zhao ◽  
Nichole Sullivan ◽  
Genshe Chen ◽  
Khanh Pham ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Task Assignment ◽  
Edge Computing ◽  
Learning Approach ◽  
Mobile Edge Computing

scholarly journals Intelligent scaling for 6G IoE services for resource provisioning

2021 ◽  
Vol 7 ◽  
pp. e755
Author(s):  
Abdullah Alharbi ◽  
Hashem Alyami ◽  
Poongodi M ◽  
Hafiz Tayyab Rauf ◽  
Seifedine Kadry
Keyword(s):  
Reinforcement Learning ◽  
Resource Provisioning ◽  
Edge Computing ◽  
Effective Model ◽  
The Internet ◽  
Mobile Edge Computing ◽  
Placement Decisions ◽  
Service Placement

The proposed research motivates the 6G cellular networking for the Internet of Everything’s (IoE) usage empowerment that is currently not compatible with 5G. For 6G, more innovative technological resources are required to be handled by Mobile Edge Computing (MEC). Although the demand for change in service from different sectors, the increase in IoE, the limitation of available computing resources of MEC, and intelligent resource solutions are getting much more significant. This research used IScaler, an effective model for intelligent service placement solutions and resource scaling. IScaler is considered to be made for MEC in Deep Reinforcement Learning (DRL). The paper has considered several requirements for making service placement decisions. The research also highlights several challenges geared by architectonics that submerge an Intelligent Scaling and Placement module.

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