计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (7): 307-314.doi: 10.11896/jsjkx.240500036

• 计算机网络 • 上一篇    下一篇

面向多用户的任务卸载和服务缓存策略研究

王翔1, 韩青海2, 梁家瑞3, 余小莉2, 吴麒1, 卿利1   

  1. 1 中国西南电子技术研究所 成都 610036
    2 重庆邮电大学通信与信息工程学院 重庆 400065
    3 军事科学院系统工程院 北京 100000
  • 收稿日期:2024-05-09 修回日期:2024-07-10 发布日期:2025-07-17
  • 通讯作者: 梁家瑞(jrliang1991@163.com)
  • 作者简介:(wangxiang12@cetc.com.cn)
  • 基金资助:
    国家自然科学基金(62071077,62301099);中国博士后科学基金(2023MD734137);重庆市自然科学基金创新发展联合基金(2022NSCQ-LZX0191)

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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摘要： 作为提供存储、计算等多维资源的新型平台,移动边缘计算(MEC)将云计算的能力部署到边缘侧,就近为用户提供低时延、低功耗的服务。然而,由于MEC服务器的计算资源有限,如何针对用户的海量数据来选择有效的任务执行方案十分重要。为此,从时延优化和终端节能两个方面对任务卸载和服务缓存进行了研究,主要针对多用户的MEC网络场景,以最小化任务延迟和终端能耗为优化目标,建立了基于该网络场景的任务卸载和服务缓存联合优化问题。在此基础上,提出了一种基于深度Q网络的任务卸载和服务缓存方案来求解优化问题。仿真结果表明,相较于其他基准方案,所提方案能够更好地提升系统服务缓存命中率,并降低终端能耗和任务延迟。

关键词: 移动边缘计算, 任务卸载, 服务缓存, 多用户单服务器, 深度Q网络

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

中图分类号: 

  • TN929
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