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): 107-114.

• 研究报告 • 上一篇    下一篇

面向流任务的低功耗多用户边缘计算

李翔,李连源,喻炜,吴博,葛欣   

  1. 中国移动
  • 收稿日期:2023-09-21 修回日期:2023-12-12 出版日期:2024-10-28 发布日期:2024-11-10
  • 通讯作者: 李连源 E-mail:lilianyuan@chinamobile.com

Energy-efficient multi-user edge computing for streaming tasks

  • Received:2023-09-21 Revised:2023-12-12 Online:2024-10-28 Published:2024-11-10

摘要: 在移动边缘计算(MEC, mobile edge computing)系统中,移动用户可以将自身的计算任务上传至接入网侧的边缘服务器,从而有效降低自身计算任务的处理开销。对MEC系统中任务数据长时间累积的情形,为保证任务完成实时性,提出了一种流任务处理方案。此方案将任务的数据收集、本地处理与卸载传输、边缘计算分离在不同时隙中进行。在此方案之下,任务大小和实际能耗都与数据收集时间长度相关。为给出系统整体的最优节能设计,研究用户完成流任务的平均功耗最小化问题,对任务完成各阶段的持续时间、多用户卸载比例和带宽分配进行联合优化。所建立优化问题为非凸问题,难以直接求解。为解决这一难点,基于块坐标下降法将求解变量分离为两部分,并进一步揭示最优解的解析性质,据此将两部分变量的求解分别简化为二分搜索和黄金分割搜索。仿真结果表明,所提方法具有极低的计算复杂度并且显著降低了系统平均功耗。

关键词: 边缘计算, 低功耗, 多用户, 任务卸载, 资源分配

Abstract: In a multi-user mobile edge computing (MEC) system, mobile users can upload their own tasks to the edge server on the access network, thereby effectively reducing the processing overhead of their tasks. In a MEC system, to ensure the real-time execution of tasks with long data collecting duration, a streaming task processing scheme is proposed, where the data collection and local computing, the offloading transmission and edge computation, are carried out in different time slots. Under this scheme, specific size of the task, more importantly energy consumption for executing the task, is related to the time length of data collection. To find the most energy-efficient way for completing the streaming tasks, the problem of minimizing the overall power consumption is formulated to jointly optimize the duration of each stage for completing the task, together with the multi-user offloading ratio and bandwidth allocation. In order to solve the intractable non-convex problem, block coordinate descent method is utilized to separate the optimization variables into two parts. Exploiting the analytical structure of the problem, optimal solution of these two parts of variables is obtained with bisection search and golden section search. Simulation results show that the proposed method has extremely low computational complexity and can significantly reduce the overall system power consumption.

Key words: mobile edge computing, energy efficiency, multiple users, task offloading, resource allocation

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