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Research on task offloading based on deep reinforcement learning in mobile edge environment
2020
MATEC Web of Conferences
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algorithm in mobile edge computing. ...
In this paper, a deep reinforcement learning algorithm is proposed to solve the task unloading problem of multi-service nodes. ...
the application prospects of deep reinforcement learning algorithm in mobile edge computing. ...
doi:10.1051/matecconf/202030903026
fatcat:36bcd2wopjhytgla6c6x7wo5em
Optimization of Task Offloading Strategy for Mobile Edge Computing Based on Multi-Agent Deep Reinforcement Learning
2020
IEEE Access
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His research interests include mobile edge computing, deep reinforcement learning and convex optimization. ...
The reference [11] chose whether to offload tasks to the edge server which serves for multiple users by deep reinforcement learning algorithm, so as to reduce energy consumption and average computing ...
doi:10.1109/access.2020.3036416
fatcat:fwfmxqfnfreqxmyhocmw3mqsbi
DMRO:A Deep Meta Reinforcement Learning-based Task Offloading Framework for Edge-Cloud Computing
[article]
2020
arXiv
pre-print
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With the continuous growth of mobile data and the unprecedented demand for computing power, resource-constrained edge devices cannot effectively meet the requirements of Internet of Things (IoT) applications ...
By aggregating the perceptive ability of deep learning, the decision-making ability of reinforcement learning, and the rapid environment learning ability of meta-learning, it is possible to quickly and ...
Classic AI methods including deep learning and reinforcement learning, can provide more reasonable and intelligent solutions to solve the offloading decision problem in edge computing. ...
arXiv:2008.09930v1
fatcat:ahggutqwsrbyvgd2hnwcuy26xi
Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results
2020
Applied Sciences
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This paper proposes a novel dynamic offloading decision method which is inspired by deep reinforcement learning (DRL). ...
In order to realize real-time communications in mobile edge computing systems, an efficient task offloading algorithm is required. ...
For the application of deep reinforcement learning to mobile edge computing, the research contributions in [8] [9] [10] [11] had been discussed about the optimization for their own objective functions ...
doi:10.3390/app10051663
fatcat:o2so2mqzjfdsdlp5p62fjse5wi
Multiuser Computing Offload Algorithm Based on Mobile Edge Computing in the Internet of Things Environment
2022
Wireless Communications and Mobile Computing
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As traditional cloud computing is not efficient enough to support large-scale computational task execution in IoT environments, a task offloading and resource allocation algorithm for mobile edge computing ...
Then, the task offloading model is formulated into a Markov decision process, and an offloading strategy based on a deep Q network (DQN) is designed to dynamically make fine tunings on the offloading proportion ...
Mobile edge computing (MEC) provides cloud computing capacity for mobile devices at the edge of networks via wireless access, which solves the problem of limited computation and energy resources for mobile ...
doi:10.1155/2022/6107893
fatcat:twaj3oibwzgj3kklcmpr57h5di
Secure and Energy-Efficient Computational Offloading Using LSTM in Mobile Edge Computing
2022
Security and Communication Networks
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In today's world, mobile edge computing is improving in various forms so as to provide better output and there is no room for simple computing architecture for MEC. ...
the scheme of edge cloud scheduling helps to optimize the edge computing offloading model. ...
offloading and resource allocation for MEC Deep reinforcement learning strategy Achieves significant reduction on the sum cost 4 Anas et al. [24] Autonomous workload balancing in cloud federation environments ...
doi:10.1155/2022/4937588
fatcat:cm54h5tfcze43btke5hdxctbqi
Deep Q-Learning Based Computation Offloading Strategy for Mobile Edge Computing
2019
Computers Materials & Continua
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deep reinforcement learning (DRL) scheme. ...
Consequently, we use deep reinforcement learning algorithm, which combines RL method Q-learning with the deep neural network (DNN) to approximate the value functions for complicated control applications ...
Conclusion In this paper, we propose a deep reinforcement learning approach for computation offloading decision issue with mobile edge computing. ...
doi:10.32604/cmc.2019.04836
fatcat:2e6iav537rcnppabz4f5c6iocq
Vehicular Edge Computing via Deep Reinforcement Learning
[article]
2020
arXiv
pre-print
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We formulate the offloading decision of multi-task in a service as a long-term planning problem, and explores the recent deep reinforcement learning to obtain the optimal solution. ...
Inspired by recent advances in machine learning, we propose a knowledge driven (KD) service offloading decision framework for Vehicle of Internet, which provides the optimal policy directly from the environment ...
Deep reinforcement learning (DRL) method combines the perceive capability of the deep learning and the decision capability of the reinforcement learning. ...
arXiv:1901.04290v3
fatcat:2ubcmtfm7ne3djdoj6pwxm6aie
Com-DDPG: A Multiagent Reinforcement Learning-based Offloading Strategy for Mobile Edge Computing
[article]
2020
arXiv
pre-print
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In this paper, we propose a novel offloading approach, Com-DDPG, for MEC using multiagent reinforcement learning to enhance the offloading performance. ...
Mobile edge computing (MEC) has been widely used to address these problems. However, there are limitations to existing methods used during computation offloading. ...
In the mobile environment, the subtask offloading decision involves a total number of (n + m) computing devices, including n mobile device and m edge servers. ...
arXiv:2012.05105v1
fatcat:tqgssard5bd7znp5yx3yiloxny
Dependent Task-Offloading Strategy Based on Deep Reinforcement Learning in Mobile Edge Computing
2023
Wireless Communications and Mobile Computing
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In mobile edge computing, there are usually relevant dependencies between different tasks, and traditional algorithms are inefficient in solving dependent task-offloading problems and neglect the impact ...
A Dependent Task-Offloading Strategy (DTOS) based on deep reinforcement learning is proposed with minimizing the weighted sum of delay and energy consumption of network services as the optimization objective ...
deep reinforcement learning to research dependent-task adaptive-offloading issues in dynamic network environments in edge computing. ...
doi:10.1155/2023/4665067
fatcat:mgl7ug24zvf6jlifxyoc4h4ny4
Ultra-Low Latency Multi-Task Offloading in Mobile Edge Computing
2021
IEEE Access
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INDEX TERMS Mobile edge computing, computation offloading, multi-server, multi-task, deep reinforcement learning, deep deterministic policy gradient. ...
Mobile edge computing (MEC) is a computation model with great potential to meet application requirements and alleviate burdens on SMDs through computation offloading. ...
His research interests include mobile edge computing, machine learning, deep reinforcement learning, and natural language processing. ...
doi:10.1109/access.2021.3061105
fatcat:zbixcq2hzrgslohtddnbbtuqva
Dynamic Offloading Method for Mobile Edge Computing of Internet of Vehicles Based on Multi-Vehicle Users and Multi-MEC Servers
2022
Electronics
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The computing offloading technology of mobile edge computing (MEC) has received extensive attention in the Internet of Vehicles (IoV) architecture. ...
Regarding the issue above, in the IoV environment where vehicle users race, this paper designs a three-layer system task offloading overhead model based on the Edge-Cloud collaboration of multiple vehicle ...
Reference [29] proposes an adaptive task offloading and resource allocation method in the MEC environment, using deep reinforcement learning to select appropriate task computing nodes for mobile users ...
doi:10.3390/electronics11152326
fatcat:6omyt55nvzc35olzilw75fcwhe
Mobile Edge Computing and Artificial Intelligence: A Mutually-Beneficial Relationship
[article]
2020
arXiv
pre-print
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On the other hand, MEC servers are utilized to avail a distributed and parallelized learning framework, namely mobile edge learning. ...
This article provides an overview of mobile edge computing (MEC) and artificial intelligence (AI) and discusses the mutually-beneficial relationship between them. ...
From Edge Computing to Edge Learning In many realistic applications, an AI algorithm is a computationally-expensive task and requires large-scale training samples. ...
arXiv:2005.03100v1
fatcat:zyu2ibxhn5fqhe2wpkfsuhwece
Artificial Intelligence based Edge Computing Framework for Optimization of Mobile Communication
2020
Journal of ISMAC
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The mobile edge system is enabled with Machine Learning techniques in order to improve the edge system intelligence, optimization of communication, caching and mobile edge computing. ...
For improving the mobile service quality and acceleration of content delivery, edge computing techniques have been providing optimal solution to bridge the device requirements and cloud capacity by network ...
Conclusion An Artificial Intelligence based edge computing framework for optimization of mobile communication is discussed in this paper with federated and deep reinforcement based machine learning schemes ...
doi:10.36548/jismac.2020.3.004
fatcat:6psc5f7kw5eina3ehh3v2xveji
Collaborative Edge Computing and Caching with Deep Reinforcement Learning Decision Agents
2020
IEEE Access
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Recently, researchers have begun to use machine learning or deep learning to optimize the computational offload strategy for edge computing. Zhang et al. ...
[17] proposed a computing resource allocation strategy based on deep reinforcement learning for URLLC edge computing networks with multiple users. Wang et al. ...
CONCLUSION AND FUTURE WORK In this paper, we consider the bandwidth, computing, and cache resources of the ENs, benefit from the deep learning and powerful learning ability and decision-making characteristics ...
doi:10.1109/access.2020.3007002
fatcat:eetpyov3rjhkxgovhgw2qxfkv4
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