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Energy-Efficient Collaborative Multi-Access Edge Computing via Deep Reinforcement Learning

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成果类型:
期刊论文
作者:
Tan, Lin;Kuang, Zhufang;Gao, Jie;Zhao, Lian
通讯作者:
Kuang, ZF
作者机构:
[Kuang, Zhufang; Tan, Lin] Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Peoples R China.
[Gao, Jie] Carleton Univ, Sch Informat Technol, Ottawa, ON K1S 5B6, Canada.
[Zhao, Lian] Toronto Metropolitan Univ, Dept Elect Comp & Biomed Engn, Toronto, ON M5B 2K3, Canada.
通讯机构:
[Kuang, ZF ] C
Cent South Univ Forestry & Technol, Sch Comp & Informat Engn, Changsha 410004, Peoples R China.
语种:
英文
关键词:
Collaborative computing offloading;deep reinforcement learning (DRL);multi-access edge computing (MEC);resource allocation
期刊:
IEEE Transactions on Industrial Informatics
ISSN:
1551-3203
年:
2023
卷:
19
期:
6
页码:
7689-7699
基金类别:
10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62072477, 61309027, 61702562 and 62071398) 10.13039/501100004735-Natural Science Foundation of Hunan Province (Grant Number: 2018JJ3888) Scientific Research Fund of Hunan Provincial Education Department (Grant Number: 18B197) National Key R&D Program of China (Grant Number: 2018YFB1700200) 10.13039/501100000038-Natural Sciences and Engineering Research Council of Canada (Grant Number: RGPIN-2020-04678) Hunan Key Laboratory of Intelligent Logistics Technology (Grant Number: 2019TP1015)
机构署名:
本校为第一且通讯机构
院系归属:
计算机与信息工程学院
摘要:
The joint problem of task offloading, collaborative computing, and resource allocation for multi-access edge computing (MEC) is a challenging issue. In this article, splitting computing tasks at MEC servers through collaboration among MEC servers and a cloud server, we investigate the joint problem of collaborative task offloading and resource allocation. A collaborative task offloading, computing resource allocation, and subcarrier and power allocation problem in MEC is formulated. The goal is to minimize the total energy consumption of the MEC system while satisfying a delay constraint. The ...

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