scholarly journals Task Offloading and Resource Allocation for Mobile Edge Computing by Deep Reinforcement Learning Based on SARSA

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 54074-54084 ◽  
Author(s):  
Taha Alfakih ◽  
Mohammad Mehedi Hassan ◽  
Abdu Gumaei ◽  
Claudio Savaglio ◽  
Giancarlo Fortino
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Offloading

scholarly journals Computing Resource Allocation Scheme of IOV using Deep Reinforcement Learning in Edge Computing Environment

2021 ◽  
Author(s):  
Yiwei Zhang ◽  
Min Zhang ◽  
Caixia Fan ◽  
Fuqiang Li ◽  
Baofang Li
Keyword(s):  
Resource Allocation ◽  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Allocation Scheme ◽  
Computing Environment ◽  
Learning Network ◽  
Resource Allocation Scheme ◽  
The Mathematical Model ◽  
Task Offloading

Abstract With the emergence and development of 5G technology, Mobile Edge Computing (MEC) has been closely integrated with Internet of Vehicles (IoV) technology, which can effectively support and improve network performance in IoV. However, the high-speed mobility of vehicles and diversity of communication quality make computing task offloading strategies more complex. To solve the problem, this paper proposes a computing resource allocation scheme based on deep reinforcement learning network for mobile edge computing scenarios in IoV. Firstly, the task resource allocation model for IoV in corresponding edge computing scenario is determined regarding the computing capacity of service nodes and vehicle moving speed as constraints. Besides, the mathematical model for task offloading and resource allocation is established with the minimum total computing cost as objective function. Then, deep Q-learning network based on deep reinforcement learning network is proposed to solve the mathematical model of resource allocation. Moreover, experience replay method is used to solve the instability of nonlinear approximate function neural network, which can avoid falling into dimension disaster and ensure the low-overhead and low-latency operation requirements of resource allocation. Finally, simulation results show that proposed scheme can effectively allocate the computing resources of IoV in edge computing environment.

Energy-Aware Task Offloading and Resource Allocation for Time-Sensitive Services in Mobile Edge Computing Systems

2021 ◽  
pp. 1-1
Author(s):  
Mingxiong Zhao ◽  
Jun-Jie Yu ◽  
Wen-Tao Li ◽  
Di Liu ◽  
Shaowen Yao ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Energy Aware ◽  
Computing Systems ◽  
Task Offloading

Task Offloading and Resource Allocation for Container-enabled Mobile Edge Computing

2021 ◽  
Author(s):  
Ao Zhou ◽  
Sisi Li ◽  
Shangguang Wang
Keyword(s):  
Resource Allocation ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Offloading

Mobile Edge Computing Based Task Offloading and Resource Allocation in Smart Grid

2021 ◽  
Author(s):  
Haie Dou ◽  
Zhichen Xu ◽  
Xue Jiang ◽  
Jingwu Cui ◽  
Baoyu Zheng
Keyword(s):  
Resource Allocation ◽  
Smart Grid ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Offloading
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