scholarly journals Using Deep Reinforcement Learning for Application Relocation in Multi-Access Edge Computing

2019 ◽  
Vol 3 (3) ◽  
pp. 71-78 ◽  
Author(s):  
Fabrizio De Vita ◽  
Giovanni Nardini ◽  
Antonio Virdis ◽  
Dario Bruneo ◽  
Antonio Puliafito ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Edge Computing ◽  
Multi Access

scholarly journals Decentralized Offloading Strategies Based on Reinforcement Learning for Multi-Access Edge Computing

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 343
Author(s):  
Chunyang Hu ◽  
Jingchen Li ◽  
Haobin Shi ◽  
Bin Ning ◽  
Qiong Gu
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Learning Model ◽  
Learning Technologies ◽  
Edge Computing ◽  
Gradient Algorithm ◽  
Computing Systems ◽  
Decentralized Execution ◽  
Multi Access ◽  
Reinforcement Learning Model

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.

scholarly journals Deep Reinforcement Learning for the Computation Offloading in MIMO-based Edge Computing

2021 ◽  
Author(s):  
Abdeladim Sadiki ◽  
Jamal Bentahar ◽  
Rachida Dssouli ◽  
Abdeslam En-Nouaary
Keyword(s):  
Reinforcement Learning ◽  
High Performance ◽  
Multiple Input Multiple Output ◽  
Edge Computing ◽  
Base Stations ◽  
Computation Offloading ◽  
Discrete Action ◽  
Computationally Intensive ◽  
Multi Access ◽  
Policy Optimization

Multi-access Edge Computing (MEC) has recently emerged as a potential technology to serve the needs of mobile devices (MDs) in 5G and 6G cellular networks. By offloading tasks to high-performance servers installed at the edge of the wireless networks, resource-limited MDs can cope with the proliferation of the recent computationally-intensive applications. In this paper, we study the computation offloading problem in a massive multiple-input multiple-output (MIMO)-based MEC system where the base stations are equipped with a large number of antennas. Our objective is to minimize the power consumption and offloading delay at the MDs under the stochastic system environment. To this end, we formulate the problem as a Markov Decision Process (MDP) and propose two Deep Reinforcement Learning (DRL) strategies to learn the optimal offloading policy without any prior knowledge of the environment dynamics. First, a Deep Q-Network (DQN) strategy to solve the curse of the state space explosion is analyzed. Then, a more general Proximal Policy Optimization (PPO) strategy to solve the problem of discrete action space is introduced. Simulation results show that the proposed DRL-based strategies outperform the baseline and state-of-the-art algorithms. Moreover, our PPO algorithm exhibits stable performance and efficient offloading results compared to the benchmark DQN strategy.

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