Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (7): 307-314.doi: 10.11896/jsjkx.240500036

• Computer Network • Previous Articles     Next Articles

Research on Multi-user Task Offloading and Service Caching Strategies

WANG Xiang1, HAN Qinghai2, LIANG Jiarui3, YU Xiaoli2, WU Qi1, QING Li1   

  1. 1 Southwest Institute of Electronic Technology, Chengdu 610036, China
    2 School of Communication and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
    3 Institute of System Engineering, AMS, PLA, Beijing 100000, China
  • Received:2024-05-09 Revised:2024-07-10 Published:2025-07-17
  • About author:WANG Xiang,born in 1988,Ph.D,engineer.His main research interests include wireless mobile communications,mobile edge storage and intelligent communication.
    LIANG Jiarui,born in 1991,Ph.D,assistant researcher.His main research interests include wireless Ad hoc networks,mobile edge computing,and network resource allocation and optimization.
  • Supported by:
    National Natural Science Foundation of China(62071077,62301099),China Postdoctoral Science Foundation(2023MD734137) and Innovation and Development Joint Fund of Natural Science Foundation of Chongqing,China(2022NSCQ-LZX0191).

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Abstract: As a novel platform providing multidimensional resources such as storage and computing,mobile edge computing(MEC) deploys the capabilities of cloud computing to the edge,offering low-latency and low-power services to users in proximity.However,due to the limited computing resources of MEC servers,selecting effective task execution strategies for massive user data is crucial.This paper investigates task offloading and service caching from the perspectives of latency optimization and terminal energy conservation.Focusing on an MEC network scenario of multiple user,the paper formulates a joint optimization pro-blem for task offloading and service caching,aiming to minimize task latency and terminal energy consumption.Subsequently,the paper proposes a solution based on deep Q-networks to address the optimization problem.Simulation results demonstrate that compared to other benchmark schemes,the proposed approach can significantly improve system service caching hit rates,reduce terminal energy consumption,and decrease task latency.

Key words: Mobile edge computing, Task offloading, Service caching, Multiple users single server, Deep Q-network

CLC Number: 

  • TN929
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