Task Offloading for UAV-based Mobile Edge Computing via Deep Reinforcement Learning

2018 ◽  
Author(s):  
Jun Li ◽  
Qian Liu ◽  
Pingyang Wu ◽  
Feng Shu ◽  
Shi Jin
Keyword(s):  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Offloading

scholarly journals Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 202573-202584
Author(s):  
Haifeng Lu ◽  
Chunhua Gu ◽  
Fei Luo ◽  
Weichao Ding ◽  
Shuai Zheng ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Multi Agent ◽  
Task Offloading

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 Research on task offloading based on deep reinforcement learning in mobile edge environment

2020 ◽  
Vol 309 ◽  
pp. 03026
Author(s):  
Xia Gao ◽  
Fangqin Xu
Keyword(s):  
Reinforcement Learning ◽  
Learning Algorithm ◽  
Edge Computing ◽  
Internet Technology ◽  
Good Effect ◽  
Mobile Edge Computing ◽  
Data Set ◽  
The Impact ◽  
Task Offloading ◽  
Reinforcement Learning Algorithm

With the rapid development of Internet technology and mobile terminals, users’ demand for high-speed networks is increasing. Mobile edge computing proposes a distributed caching approach to deal with the impact of massive data traffic on communication networks, in order to reduce network latency and improve user service quality. In this paper, a deep reinforcement learning algorithm is proposed to solve the task unloading problem of multi-service nodes. The simulation platform iFogSim and data set Google Cluster Trace are used to carry out experiments. The final results show that the task offloading strategy based on DDQN algorithm has a good effect on energy consumption and cost, it has verified the application prospect of deep reinforcement learning algorithm in mobile edge computing.

scholarly journals Task Offloading and Trajectory Control for UAV-Assisted Mobile Edge Computing Using Deep Reinforcement Learning

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Lu Zhang ◽  
Zi-Yan Zhang ◽  
Luo Min ◽  
Chao Tang ◽  
Hong-Ying Zhang ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Trajectory Control ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Offloading

scholarly journals Task Offloading with Power Control for Mobile Edge Computing Using Reinforcement Learning-Based Markov Decision Process

2020 ◽  
Vol 2020 ◽  
pp. 1-6 ◽  
Author(s):  
Bingxin Zhang ◽  
Guopeng Zhang ◽  
Weice Sun ◽  
Kun Yang
Keyword(s):  
Reinforcement Learning ◽  
Power Control ◽  
Markov Decision Process ◽  
Decision Process ◽  
Search Algorithm ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Multiple User ◽  
Markov Decision ◽  
Task Offloading

This paper proposes an efficient computation task offloading mechanism for mobile edge computing (MEC) systems. The studied MEC system consists of multiple user equipment (UEs) and multiple radio interfaces. In order to maximize the number of UEs benefitting from the MEC, the task offloading and power control strategy for a UE is optimized in a joint manner. However, the problem of finding the optimal solution is NP-hard. We then reformulate the problem as a Markov decision process (MDP) and develop a reinforcement learning- (RL-) based algorithm to solve the MDP. Simulation results show that the proposed RL-based algorithm achieves a near-optimal performance compared to the exhaustive search algorithm, and it also outperforms the received signal strength- (RSS-) based method no matter from the standpoint of the system (as it leads to a larger number of beneficial UEs) or an individual (as it generates a lower computation overhead for a UE).

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