Mobile edge computing task distribution and offloading algorithm based on deep reinforcement learning in internet of vehicles

2021 ◽  
Author(s):  
Jianxi Wang ◽  
Liutao Wang
Keyword(s):  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Internet Of Vehicles ◽  
Task Distribution

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 Cost-Effective Resource Sharing in an Internet of Vehicles-Employed Mobile Edge Computing Environment

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 594 ◽  
Author(s):  
Tri Nguyen ◽  
Tien-Dung Nguyen ◽  
Van Nguyen ◽  
Xuan-Qui Pham ◽  
Eui-Nam Huh
Keyword(s):  
Video Streaming ◽  
Resource Sharing ◽  
Vehicular Network ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Users ◽  
Vehicle Speed ◽  
Mobile Edge Computing ◽  
Internet Of Vehicles ◽  
The Cost

By bringing the computation and storage resources close proximity to the mobile network edge, mobile edge computing (MEC) is a key enabling technology for satisfying the Internet of Vehicles (IoV) infotainment applications’ requirements, e.g., video streaming service (VSA). However, the explosive growth of mobile video traffic brings challenges for video streaming providers (VSPs). One known issue is that a huge traffic burden on the vehicular network leads to increasing VSP costs for providing VSA to mobile users (i.e., autonomous vehicles). To address this issue, an efficient resource sharing scheme between underutilized vehicular resources is a promising solution to reduce the cost of serving VSA in the vehicular network. Therefore, we propose a new VSA model based on the lower cost of obtaining data from vehicles and then minimize the VSP’s cost. By using existing data resources from nearby vehicles, our proposal can reduce the cost of providing video service to mobile users. Specifically, we formulate our problem as mixed integer nonlinear programming (MINP) in order to calculate the total payment of the VSP. In addition, we introduce an incentive mechanism to encourage users to rent its resources. Our solution represents a strategy to optimize the VSP serving cost under the quality of service (QoS) requirements. Simulation results demonstrate that our proposed mechanism is possible to achieve up to 21% and 11% cost-savings in terms of the request arrival rate and vehicle speed, in comparison with other existing schemes, respectively.

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