数据与计算发展前沿 ›› 2020, Vol. 2 ›› Issue (3): 126-136.doi: 10.11871/jfdc.issn.2096-742X.2020.03.011

• 技术与应用 • 上一篇    下一篇

多基站多用户场景的移动边缘计算卸载策略

时月茹1,2(),李俊1,*()   

  1. 1. 中国科学院计算机网络信息中心,北京 100190
    2. 中国科学院大学,北京 100049
  • 收稿日期:2020-03-02 出版日期:2020-06-20 发布日期:2020-08-19
  • 通讯作者: 李俊
  • 作者简介:时月茹,中国科学院计算机网络信息中心,在读硕士研究生,主要研究方向为云计算、移动边缘计算。
    本文贡献:理论分析,代码实现及测试,论文写作。
    Shi Yueru is currently a master student at Computer Network Information Center of Chinese Academy of Sciences. Her main research interests are cloud computing and mobile edge computing.
    In this paper, she undertakes the following tasks: theoretical analysis, code implementation, testing, and paper writing.
    E-mail: shiyueru@cnic.cn|李俊,中国科学院计算机网络信息中心,研究员,博士生导师,中国科学院特聘研究员。主要研究方向为人工智能和大数据应用、互联网体系结构。
    本文贡献:研究指导,论文结构组织。
    Li Jun, specially appointed researcher of Chinese Academy of Sciences, is currently a research fellow and PhD supervisor at Computer Network Information Center of Chinese Academy of Sciences. His main research interests are artificial intelligence and big data technical applications and future Internet architecture.
    In this paper, he undertakes the following tasks: research guidance, and paper structure organization.
    E-mail: jlee@cstnet.cn
  • 基金资助:
    国家重点研发计划资助项目(2017YFB1401500)

Computation Offloading Strategy for Multi-Base Station and Multi-User Equipment Mobile Edge Computing

Shi Yueru1,2(),Li Jun1,*()   

  1. 1. Computer Network Information Center, Chinese Academy of Sciences, Beijing 100190, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-03-02 Online:2020-06-20 Published:2020-08-19
  • Contact: Li Jun

摘要:

【目的】计算卸载是移动边缘计算(MEC)的重要研究领域,弥补了设备在存储、计算等方面的不足,受到广泛关注。本文研究MEC密集网络的计算卸载策略。【方法】针对多基站多用户场景,提出了具备服务缓存和资源调度特征的卸载模型,采用动态规划和博弈论对缓存问题和通信计算资源的联合分配问题分别进行处理,实现用户之间相互满意的纳什均衡状态。【结果】通过仿真实验,证明了该策略的有效性,明显降低开销,提升系统性能,更好地满足用户需求。【结论】适用于移动边缘计算场景,为后续的计算卸载研究提供了理论和实践支持,下一步工作将引入激励机制对用户卸载行为的影响。

关键词: 移动边缘计算, 计算卸载, 服务缓存, 资源调度

Abstract:

[Objective] Computation offloading is an important research area of Mobile Edge Computing (MEC). It makes up for the shortcomings of devices in storage and computation, thus receiving extensive attention. This paper studies the computation offloading strategy for MEC with dense network. [Methods] For the multi-base station and multi-user equipment scenario, we construct a computation offloading model with service caching and resource allocating features, and adopt dynamic programming and game theory to solve the caching problem and jointly allocate radio and computational resources. Finally, a Nash equilibrium state of mutual satisfaction achieves among users. [Results] Simulation experiments show that the proposed strategy is efficient to reduce overhead, improve system performance and can receive better satisfaction. [Conclusions] It is suitable for mobile edge computing scenario, and provides theoretical and practical supports for subsequent research on computation offloading. In the next step, we will consider the incentive mechanism to users’ behavior when discussing computation offloading.

Key words: mobile edge computing, computation offloading, service caching, resource allocating