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Vehicular Edge Computing via Deep Reinforcement Learning [article]

Qi Qi, Zhanyu Ma
2020 arXiv   pre-print
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.  ...  Although the computation capability of the vehicle is limited, multi-type of edge computing nodes provide heterogeneous resources for vehicular services.When offloading the complicated service to the vehicular  ...  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

Privacy-Preserved Task Offloading in Mobile Blockchain with Deep Reinforcement Learning [article]

Dinh C. Nguyen, Pubudu N. Pathirana, Ming Ding, Aruna Seneviratne
2019 arXiv   pre-print
To meet this challenge, we propose a mobile edge computing (MEC) based blockchain network where multi-mobile users (MUs) act as miners to offload their mining tasks to a nearby MEC server via wireless  ...  We first propose a reinforcement learning (RL)-based offloading scheme which enables MUs to make optimal offloading decisions based on blockchain transaction states and wireless channel qualities between  ...  learning and then deep reinforcement learning to achieve the optimal offloading performances for miners.  ... 
arXiv:1908.07467v1 fatcat:gwp3l7idqrflfpyrly22ukaita

Artificial Intelligence-Empowered Edge of Vehicles: Architecture, Enabling Technologies, and Applications

Hongjing Ji, Osama Alfarraj, Amr Tolba
2020 IEEE Access  
To substantially reduce the latency and the energy consumption, application work is offloaded from a mobile device to a remote cloud or a nearby mobile edge cloud for processing.  ...  Therefore, mobile edge computing (MEC) has the advantages of effectively utilizing idle computing and storage resources at the edge of the network and reducing the network transmission delay.  ...  a deep reinforcement learning method that is based on a multi-time-scale framework.  ... 
doi:10.1109/access.2020.2983609 fatcat:b45abdrxbracnbfpvtvtu5uxui

In-Edge AI: Intelligentizing Mobile Edge Computing, Caching and Communication by Federated Learning [article]

Xiaofei Wang, Yiwen Han, Chenyang Wang, Qiyang Zhao, Xu Chen, Min Chen
2019 arXiv   pre-print
Learning techniques and Federated Learning framework with the mobile edge systems, for optimizing the mobile edge computing, caching and communication.  ...  In order to bring more intelligence to the edge systems, compared to traditional optimization methodology, and driven by the current deep learning techniques, we propose to integrate the Deep Reinforcement  ...  Fig. 3 . 3 Taxonomy of applying Deep Reinforcement Learning in mobile edge system coordinating the edge nodes.  ... 
arXiv:1809.07857v2 fatcat:2sav5fnozbc3rd2atcgpxzg7jq

Energy-Aware Multi-Server Mobile Edge Computing: A Deep Reinforcement Learning Approach [article]

Navid Naderializadeh, Morteza Hashemi
2019 arXiv   pre-print
We investigate the problem of computation offloading in a mobile edge computing architecture, where multiple energy-constrained users compete to offload their computational tasks to multiple servers through  ...  We propose a multi-agent deep reinforcement learning algorithm, where each server is equipped with an agent, observing the status of its associated users and selecting the best user for offloading at each  ...  Fig. 1 : 1 Mobile Edge Computing (MEC) system architecture. Fig. 2 : 2 Deep reinforcement learning model. Fig. 3 : 3 System model.  ... 
arXiv:1912.10485v1 fatcat:avqegyh76nbkxll63wtz432tkq

A new task offloading algorithm in edge computing

Zhenjiang Zhang, Chen Li, ShengLung Peng, Xintong Pei
2021 EURASIP Journal on Wireless Communications and Networking  
expansion, and proposes a multi-agent load balancing distribution based on deep reinforcement learning DTOMALB, a distributed task allocation algorithm, can perform a reasonable offload method for this  ...  In view of the new architecture after dating edge computing, this paper focuses on the task offloading in edge computing, from task migration in multi-user scenarios and edge server resource management  ...  Dong et al. proposed an intelligent offloading system for vehicle edge computing based on deep reinforcement learning.  ... 
doi:10.1186/s13638-021-01895-6 fatcat:jvc4npe5q5gxrni76eyacnfpee

Blockchain as a Service for Multi-Access Edge Computing: A Deep Reinforcement Learning Approach [article]

Dinh C Nguyen, Pubudu N Pathirana, Ming Ding, Aruna Seneviratne
2019 arXiv   pre-print
We then propose a novel Deep Reinforcement Learning (DRL) approach using a double deep Q-network (DQN) algorithm to solve the proposed problem.  ...  As an emerging paradigm, Multi-access Edge Computing (MEC) has been widely used to provide computation and storage resources to mobile user equipments (UE) at the edge of the network for improving the  ...  Index Terms-Blockchain, Multi-access Edge Computing (MEC), smart contract, offloading, blockchain mining, Deep Reinforcement Learning (DRL). I.  ... 
arXiv:2001.08165v1 fatcat:qcdnfs67fvd3hovszrkei34ada

Multi-Server Multi-User Multi-Task Computation Offloading for Mobile Edge Computing Networks

Liang Huang, Xu Feng, Luxin Zhang, Liping Qian, Yuan Wu
2019 Sensors  
This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) offload their computation tasks to multiple edge servers and one cloud server.  ...  Considering different real-time computation tasks at different WDs, every task is decided to be processed locally at its WD or to be offloaded to and processed at one of the edge servers or the cloud server  ...  Figure 1 . 1 System Model of a multi-server multi-user multi-task mobile edge computing (MEC) network. = 1 if WD n offloads its task m to the server k.  ... 
doi:10.3390/s19061446 fatcat:fouh2v44kzfmbk5euxpnvwilla

Advanced Deep Learning-based Computational Offloading for Multilevel Vehicular Edge-Cloud Computing Networks

Mashael Khayyat, Ibrahim A. Elgendy, Ammar Muthanna, Abdullah Alshahrani, Soltan Alharbi, Andery Koucheryavy
2020 IEEE Access  
INDEX TERMS Computation offloading, vehicular edge-cloud computing, autonomous vehicles, 5G, resource allocation, deep reinforcement learning.  ...  Therefore, an equivalent reinforcement learning form is generated and we propose a distributed deep learning algorithm to find the near-optimal computational offloading decisions in which a set of deep  ...  Then, the deep reinforcement learning method is presented in detail to craft a computational offloading decision for multilevel vehicular edge-cloud computing networks. A.  ... 
doi:10.1109/access.2020.3011705 fatcat:7b3req4nujdnnmswv4cwdxrklq

2021 Index IEEE Transactions on Parallel and Distributed Systems Vol. 32

2022 IEEE Transactions on Parallel and Distributed Systems  
., +, TPDS June 2021 1277-1292 Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning.  ...  ., +, TPDS April 2021 746-759 Fast Adaptive Task Offloading in Edge Computing Based on Meta Reinforcement Learning.  ... 
doi:10.1109/tpds.2021.3107121 fatcat:e7bh2xssazdrjcpgn64mqh4hb4

Com-DDPG: A Multiagent Reinforcement Learning-based Offloading Strategy for Mobile Edge Computing [article]

Honghao Gao and Xuejie Wang and Xiaojin Ma and Wei Wei and Shahid Mumtaz
2020 arXiv   pre-print
Mobile edge computing (MEC) has been widely used to address these problems. However, there are limitations to existing methods used during computation offloading.  ...  In this paper, we propose a novel offloading approach, Com-DDPG, for MEC using multiagent reinforcement learning to enhance the offloading performance.  ...  For the MEC environment, mobile devices transmit computing tasks as messages to the edge level and implement storage and computation processes.  ... 
arXiv:2012.05105v1 fatcat:tqgssard5bd7znp5yx3yiloxny

Adaptive Real-Time Offloading Decision-Making for Mobile Edges: Deep Reinforcement Learning Framework and Simulation Results

SooHyun Park, Dohyun Kwon, Joongheon Kim, Youn Kyu Lee, Sungrae Cho
2020 Applied Sciences  
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

Deep Reinforcement Learning-Based Workload Scheduling for Edge Computing

Tao Zheng, Jian Wan, Jilin Zhang, Congfeng Jiang
2022 Journal of Cloud Computing: Advances, Systems and Applications  
To tackle this problem, we proposed a deep reinforcement learning(DRL)-based workload scheduling approach with the goal of balancing the workload, reducing the service time and the failed task rate.  ...  AbstractEdge computing is a new paradigm for providing cloud computing capacities at the edge of network near mobile users.  ...  To solve this problem, Combining deep learning and reinforcement learning, DRL algorithms, such as DQN [17] , DDPG [18] and PPO [19] , becomes useful for handling the problems of complexity and high  ... 
doi:10.1186/s13677-021-00276-0 fatcat:ogxfzvqaj5ecpk5mhvlmwxsbmi

2020 Index IEEE Transactions on Wireless Communications Vol. 19

2020 IEEE Transactions on Wireless Communications  
., Machine Learning-Enabled LOS/NLOS Identification for MIMO Systems in Dynamic Environments; TWC June 2020 3643-3657 Huang, C., see Yang, M., TWC Sept. 2020 5860-5874 Huang, D., Tao, X., Jiang, C.,  ...  Lin, P., +, TWC March 2020 1977-1989 Offloading and Resource Allocation With General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approach.  ...  Wang, S., +, TWC April 2020 2601-2612 Offloading and Resource Allocation With General Task Graph in Mobile Edge Computing: A Deep Reinforcement Learning Approach.  ... 
doi:10.1109/twc.2020.3044507 fatcat:ie4rwz4dgvaqbaxf3idysubc54

Fast Adaptive Task Offloading in Edge Computing based on Meta Reinforcement Learning [article]

Jin Wang, Jia Hu, Geyong Min, Albert Y. Zomaya, Nektarios Georgalas
2020 arXiv   pre-print
Multi-access edge computing (MEC) aims to extend cloud service to the network edge to reduce network traffic and service latency.  ...  Recently, many deep reinforcement learning (DRL) based methods have been proposed to learn offloading policies through interacting with the MEC environment that consists of UE, wireless channels, and MEC  ...  [10] focused on the multi-user multi-edge-node computation offloading problem by using deep Q-learning. Chen et al.  ... 
arXiv:2008.02033v4 fatcat:shfvu7t5srcipl3klkapktymzq
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