基于博弈论和启发式算法的超密集网络边缘计算卸载

doi:10.19678/j.issn.1000-3428.0063734

摘要/Abstract

摘要： 超密集网络与边缘计算相结合时，高密度的基站分布可能会对同一用户重复覆盖，该用户选择不同基站进行卸载将会对系统性能产生不同影响，由此引出卸载对象选取问题。同时边缘计算可以将部分任务卸载到边缘服务器进行处理，选择合适的卸载比例能够显著降低所需的时延和能耗，由此引出卸载比例选取问题。提出一种超密集网络环境中基于博弈论和启发式算法的边缘计算卸载策略。针对卸载对象选取问题，根据边缘服务器到用户之间的距离和工作负载定义偏好度指标，各用户根据偏好度进行博弈后选择卸载对象，并对用户进行分组，将原问题分解为若干个并行的子问题。针对卸载比例选取问题，基于萤火虫群优化算法对各用户的卸载比例进行优化，得到适当的卸载比例。与全本地处理（ALP）策略、全卸载策略（AOS）和基于粒子群优化（PSO）算法的卸载策略进行对比，实验结果表明，ALP和AOS策略在总能耗和平均时延上具有一定的局限性，相比基于PSO的卸载策略，所提策略的时延降低22%，能耗降低20%，可以有效减少系统损失。

关键词: 边缘计算, 计算卸载, 超密集网络, 博弈论, 萤火虫群优化算法

Abstract: When an Ultra-Dense Network(UDN) is combined with edge computing, the distribution of high-density base stations may repeatedly cover the same user.The base station chosen by the user for offloading affects the system's performance, which leads to the problem of offloading object selection.Simultaneously, edge computing can offload tasks to the edge server for processing.Choosing an appropriate offloading proportion significantly reduces the required delay and energy consumption;thus, selecting the offloading proportion is a challenge.An edge computing offloading strategy based on game theory and a heuristic algorithm in a UDN environment is proposed.For the offloading object selection problem, the preference function is first defined according to the distance between the edge server, user, and workload.Each user then selects the offloading object after playing a game according to their preference and groups the users.The original problem is decomposed into several parallel subproblems.To address the problem of offloading proportion selection, the offloading proportion of each user is optimized based on the Glowworm Swarm Optimization(GSO) algorithm, and the appropriate offloading proportion is obtained.Through a simulation experiment, compared with the All-Local Processing(ALP) strategy, All-Offloading Strategy(AOS), and offloading strategy based on Particle Swarm Optimization(PSO) algorithm, the delay and energy consumption of the ALP and AOS strategies have certain limitations in terms of the total energy consumption and average delay, respectively.Compared with the offloading strategy based on PSO, the delay of the proposed strategy is reduced by 22% and the energy consumption is reduced by 20%.Experimental results show that this strategy can effectively reduce system loss.

Key words: edge computing, computing offloading, Ultra-Dense Network(UDN), game theory, Glowworm Swarm Optimization (GSO) algorithm

中图分类号:

TP393

刘振鹏, 郭超, 王仕磊, 陈杰, 李小菲. 基于博弈论和启发式算法的超密集网络边缘计算卸载[J]. 计算机工程, 2022, 48(12): 54-61,71.

LIU Zhenpeng, GUO Chao, WANG Shilei, CHEN Jie, LI Xiaofei. Edge Computing Offloading of Ultra-Dense Network Based on Game Theory and Heuristic Algorithm[J]. Computer Engineering, 2022, 48(12): 54-61,71.

https://www.ecice06.com/CN/Y2022/V48/I12/54

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