北京邮电大学学报

  • EI核心期刊

北京邮电大学学报 ›› 2024, Vol. 47 ›› Issue (5): 44-50.

• 论文 • 上一篇    下一篇

区块链中基于数据压缩的移动边缘计算卸载策略研究

韩冰1,尹沛桐2,叶迎晖1,卢光跃1   

  1. 1. 西安邮电大学
    2. 轨道交通工程信息化国家重点实验室(中铁一院)
  • 收稿日期:2023-08-29 修回日期:2024-01-30 出版日期:2024-10-28 发布日期:2024-11-10
  • 通讯作者: 叶迎晖 E-mail:connectyyh@126.com
  • 基金资助:
    中铁一院科研项目;国家自然科学基金

Research on Offloading Strategy of Mobile Edge Computing by Data Compression in Blockchain

  • Received:2023-08-29 Revised:2024-01-30 Online:2024-10-28 Published:2024-11-10
  • Contact: Ynghui Ye E-mail:connectyyh@126.com

摘要: 移动边缘计算(Mobile Edge Computing,MEC)与区块链的结合,可以增强“挖矿”节点的算力。数据压缩可减少节点卸载数据的大小,进而降低卸载时延。利用这一优点,本文将数据压缩引入支持区块链的移动边缘计算网络中,并面向共用MEC服务器计算资源和专用MEC服务器计算资源两种场景依次设计满足节点挖矿需求的计算时延最小化卸载策略。针对共用MEC服务器计算资源方案,通过联合优化用户卸载率、压缩率来建立系统计算时延最小化的多维资源分配问题,由于存在变量耦合与max-max函数,建立的问题非凸,通过引入松弛变量和辅助变量,将问题转化为凸问题从而求得最优解;针对专用MEC服务器计算资源方案,联合优化卸载率、压缩率和计算资源建立最小化计算时延的资源分配问题,由于存在变量耦合与max-max函数使优化问题非凸,利用松弛变量和辅助变量对问题进行转化,并借助块坐标下降(Block Coordinate Descent,BCD)算法将问题分解为两个凸问题,然后提出一种基于BCD的迭代算法从而求得最优解。最后,通过仿真验证所提算法的正确性以及所提策略在计算时延方面的优越性。

关键词: 物联网, 边缘计算, 区块链, 资源分配, 计算卸载

Abstract: The combination of Mobile Edge Computing (MEC) and blockchain can enhance the computing power of “mining” nodes. Data compression can reduce the size of uninstalled data, which reduces the uninstallation delay. Using this advantage, this paper introduces data compression into the mobile edge computing network that supports blockchain, and designs a computing delay minimization offloading strategy to meet the requirements of node mining for two scenarios: shared MEC server computing resources and dedicated MEC server computing resources. For the shared MEC server computing resource scheme, a multi-dimensional resource allocation problem with minimum system computing delay was established by jointly optimizing the user offloading rate and compression rate. Due to the existence of variable coupling and max-max function, the established problem was not convex. By introducing relaxation variables and auxiliary variables, the problem was transformed into a convex problem and the optimal solution was obtained. For the dedicated MEC server computing resource scheme, jointly optimize the unloading rate, compression rate and computing resources to establish a resource allocation problem that minimizes computing delay. Due to the existence of variable coupling and max-max function, the optimization problem is non-convex, and the problem is transformed by relaxation variables and auxiliary variables. And with the aid of Block Coordinate Descent (BCD) algorithm to the problem is decomposed into two convex problems, then puts forward an iterative algorithm based on BCD to get the optimal solution. Finally, the correctness of the proposed algorithm and the superiority of the proposed strategy in calculating the delay are verified by simulation.

Key words: Internet of Things, Edge computing, Blockchain, Resource allocation, Computational offloading

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