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Author:

Fang, Chao (Fang, Chao.) | Hu, Zhaoming (Hu, Zhaoming.) | Meng, Xiangheng (Meng, Xiangheng.) | Tu, Shanshan (Tu, Shanshan.) | Wang, Zhuwei (Wang, Zhuwei.) | Zeng, Deze (Zeng, Deze.) | Ni, Wei (Ni, Wei.) | Guo, Song (Guo, Song.) | Han, Zhu (Han, Zhu.)

Indexed by:

EI Scopus SCIE

Abstract:

With the proliferation of mobile devices (e.g., vehicles and smartphones), rich media content services from massive users lead to high network resource consumption and energy usage. How to effectively allocate heterogeneous network resources and achieve green content delivery is a major challenge to be addressed in urgency, especially when vehicular users are involved and the spatiotemporal distribution of the content requests can change drastically. In this article, we propose a new deep reinforcement learning (DRL)-aided task offloading and resource allocation scheme, named TORA-DRL, to minimize power consumption in cloud-edge cooperation environments, where in-network caching and request aggregation are incorporated to reduce replicated transmissions of network contents. Based on the history of content requests and available network resources in the system, TORA-DRL jointly optimizes the decisions about task offloading, as well as computing, caching and communication resource allocation, adapting to the changes in network states and user requirements. Simulation results demonstrate that the proposed TORA-DRL solution converges fast and has much higher energy efficiency than the existing popular cloud-edge cooperation schemes under different network environments.

Keyword:

task offloading deep reinforcement learning energy efficiency Cloud-edge cooperation resource allocation

Author Community:

  • [ 1 ] [Fang, Chao]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 2 ] [Hu, Zhaoming]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 3 ] [Meng, Xiangheng]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Tu, Shanshan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 5 ] [Wang, Zhuwei]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 6 ] [Fang, Chao]Purple Mt Labs, Nanjing 211111, Peoples R China
  • [ 7 ] [Zeng, Deze]China Univ Geosci, Sch Comp Sci, Wuhan 430074, Peoples R China
  • [ 8 ] [Ni, Wei]CSIRO, Data61, Marsfield, NSW 2122, Australia
  • [ 9 ] [Guo, Song]Hong Kong Polytech Univ, Dept Comp, Hong Kong, Peoples R China
  • [ 10 ] [Han, Zhu]Univ Houston, Dept Elect & Comp Engn, Houston, TX 77004 USA
  • [ 11 ] [Han, Zhu]Kyung Hee Univ, Dept Comp Sci & Engn, Seoul 446701, South Korea

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Source :

IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY

ISSN: 0018-9545

Year: 2023

Issue: 12

Volume: 72

Page: 16195-16207

6 . 8 0 0

JCR@2022

Cited Count:

WoS CC Cited Count: 26

SCOPUS Cited Count: 42

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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