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计算机系统应用英文版:2023,32(4):262-267
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基于移动边缘计算的任务卸载优化
(1.上海电力大学 电子与信息工程学院, 上海 201306;2.国网信息通信产业集团有限公司, 北京 102200;3.上海电力大学 电气工程学院, 上海 201306)
Task Offload Optimization Based on Mobile Edge Computing
(1.College of Electronic Information Engineering, Shanghai University of Electric Power, Shanghai 201306, China;2.State Grid Information and Telecommunication Group Co. Ltd., Beijing 102200, China;3.College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 201306, China)
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Received:August 29, 2022    Revised:September 27, 2022
中文摘要: 随着智慧物联体系的发展, 物联网中应用程序的种类与数量不断增加. 在移动边缘计算(mobile edge computing, MEC)中, 通过允许移动用户将任务卸载至附近MEC服务器以加快移动应用程序的速度. 本文通过考虑不同任务属性、用户的移动性和时间延迟约束模拟移动边缘场景. 根据用户移动轨迹, 将目标建模为寻找满足时延约束条件且在卸载过程中产生最小能耗MEC服务器优化模型, 并提出一种最小能耗卸载算法求解该问题的最优解. 仿真结果表明, 在约束条件下, 提出的算法可以找到在用户移动轨迹中产生最小能耗的MEC服务器, 并显著降低任务卸载过程的能耗与时延, 提高应用程序服务质量.
Abstract:With the development of the smart Internet of things (IoT) system, the type and number of applications in the IoT continue to increase. In mobile edge computing (MEC), mobile applications are accelerated by allowing mobile users to offload tasks to nearby MEC servers. This study simulates mobile edge scenarios by analyzing task attributes, user mobility, and delay constraints. In addition, according to the moving trajectory of users, the goal is modeled. Specifically, it aims to find an optimization model for MEC servers that satisfies the delay constraints and generates the minimum energy consumption during the offloading. The study also proposes a minimum energy offloading algorithm to find the optimal solution to this problem. The simulation results show that under the constraints, the proposed algorithm can find a MEC server that generates the minimum energy consumption in the moving trajectory of users, significantly reduce the energy consumption and delay during task offloading, and improve the application service quality.
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基金项目:国家自然科学基金面上项目(61872239)
引用文本:
彭昇,赵建保,魏敏捷,秦伦明.基于移动边缘计算的任务卸载优化.计算机系统应用,2023,32(4):262-267
PENG Sheng,ZHAO Jian-Bao,WEI Min-Jie,QIN Lun-Ming.Task Offload Optimization Based on Mobile Edge Computing.COMPUTER SYSTEMS APPLICATIONS,2023,32(4):262-267