边缘计算场景中基于粒子群优化算法的计算卸载

doi:10.13229/j.cnki.jdxbgxb20210328

摘要/Abstract

摘要：

为满足边缘计算场境下用户终端设备对密集型任务处理的低时延和低能耗需求，本文设计了时延模型、能耗模型以及卸载优化模型，给出了一种基于改进型粒子群优化算法的卸载方案，并用实验验证了本文方案的优良性能。首先，对PSO进行了一些改进，提出了改进型的粒子群优化算法（UPSO）；其次，将UPSO与遗传算法（Genetic algorithm，GA）结合，提出了一种融合遗传算法的改进型粒子群优化算法（GA-UPSO）求解单用户多任务场景下的卸载决策问题。在Matlab上进行的仿真实验结果表明：本文卸载方案在时延和能耗方面均优于基于遗传算法的卸载方案和基于标准粒子群优化算法的卸载方案。

关键词: 边缘计算, 计算卸载, 时延, 能耗, 遗传算法, 粒子群优化算法

Abstract:

In order to meet the requirements of low delay and low energy consumption of user terminal equipment for intensive task processing in edge computing environment， the time delay model， energy consumption model and offloading optimization model were designed， and an offloading scheme based on improved particle swarm optimization algorithm was given， and the excellent performance of the proposed scheme was verified by experiments.Firstly, the PSO is improved and an improved particle swarm optimization (UPSO) algorithm is proposed. Secondly, by combining UPSO with Genetic algorithm (GA), an improved particle swarm optimization algorithm (GA-UPSO) is proposed to solve the unloading decision problem in the single-user multi-task scenario. The simulation results on Matlab show that the proposed offloading scheme is superior to the offloading scheme based on genetic algorithm and the offloading scheme based on standard particle swarm optimization algorithm in terms of time delay and energy consumption.

Key words: edge computing, computing offloading, time delay, energy consumption, genetic algorithm, particle swarm optimization algorithm

中图分类号:

TP391

朱思峰,赵明阳,柴争义. 边缘计算场景中基于粒子群优化算法的计算卸载[J]. 吉林大学学报(工学版), 2022, 52(11): 2698-2705.

Si-feng ZHU,Ming-yang ZHAO,Zheng-yi CHAI. Computing offloading scheme based on particle swarm optimization algorithm in edge computing scene[J]. Journal of Jilin University(Engineering and Technology Edition), 2022, 52(11): 2698-2705.

图/表 9

图1

图2

表1

参数设置"

参数	值
B/MHz	10~50
$f j$ /GHz	50~60
$f l c$ /GHz	7
$σ 2$ /dBm	10^-9
$P i D$ /dBm	30~80
GA交叉概率p_c	0.8
GA变异概率p_m	0.05
$c 1$ 、 $c 2$	1.5
PSO惯性权重w	0.8
算法迭代次数G	50

表1

图3

图4

图5

图6

图7

图8

参考文献 20

1	谢人超, 廉晓飞, 贾庆民, 等. 移动边缘计算卸载技术综述[J]. 通信学报, 2018, 39(11): 138-155.
	Xie Ren-chao, Lian Xiao-fei, Jia Qing-min, et al. Survey on computation offloading in mobile edge computing[J]. Journal of Communications, 2018, 39(11): 138-155.
2	Khan A R, Othman M, Madani S A, et al. A survey of mobile cloud computing application models[J]. IEEE Communications Surveys & Tutorials, 2014, 16(1): 393-413.
3	施巍松, 张星洲, 王一帆, 等. 边缘计算:现状与展望[J]. 计算机研究与发展, 2019, 56(1): 69-89.
	Shi Wei-song, Zhang Xing-zhou, Wang Yi-fan, et al. Edge computing: state-of-the-art and future directions[J]. Journal of Computer Research and Development, 2019, 56(1): 69-89.
4	Nasir A, Zhang Y, Taherkordi A, et al. Mobile edge computing: a survey[J]. IEEE Internet of Things Journal, 2018, 5(1): 450-465.
5	Mach P, Becvar Z. Mobile edge computing: a survey on architecture and computation offloading[J]. IEEE Communications Surveys & Tutorials, 2017, 19(3): 1628-1656.
6	Bouet M, Conan V. Mobile edge computing resources optimization: a geo-clustering approach[J]. IEEE Transactions on Network and Service Management, 2018, 15(2): 787-796.
7	Carvalho G H S, Woungang I, Anpalagan A, et al. Analysis of joint parallelism in wireless and cloud domains on mobile edge computing over 5G systems[J]. Journal of Communications and Networks, 2018, 20(6): 565-577.
8	Tang L, He S B. Multi-user computation offloading in mobile edge computing: a behavioral perspective[J]. IEEE Network, 2018, 32(1): 48-53.
9	Wang F, Xu J, Wang X, et al. Joint offloading and computing optimization in wireless powered mobile-edge computing systems[J]. IEEE Transactions on Wireless Communications, 2017, 17(3): 1784-1797.
10	夏士超, 姚枝秀, 鲜永菊, 等. 移动边缘计算中分布式异构任务卸载算法[J]. 电子与信息学报, 2020, 42(12): 2891-2898.
	Xia Shi-chao, Yao Zhi-xiu, Xian Yong-ju, et al. A distributed heterogeneous task offloading methodology for mobile edge computing[J]. Journal of Electronics & Information Technology, 2020, 42(12): 2891-2898.
11	芦效峰, 廖钰盈, Pietro L, 等. 一种面向边缘计算的高效异步联邦学习机制[J]. 计算机研究与发展, 2020, 57(12): 2571-2582.
	Lu Xiao-feng, Liao Yu-ying, Pietro L, et al. An asynchronous federated learning mechanism for edge network computing[J]. Journal of Computer Research and Development, 2020, 57(12): 2571-2582.
12	张海波, 栾秋季, 朱江, 等. 基于移动边缘计算的V2X任务卸载方案[J]. 电子与信息学报, 2018, 40(11): 2736-2743.
	Zhang Hai-bo, Luan Qiu-ji, Zhu Jiang, et al. V2X task offloading scheme based on mobile edge computing[J]. Journal of Electronics & Information Technology, 2018, 40(11): 2736-2743.
13	孟浩, 霍如, 郭倩影, 等. 基于机器学习的MEC随机任务迁移算法[J]. 北京邮电大学学报, 2019, 42(2): 25-30.
	Meng Hao, Huo Ru, Guo Qian-ying, et al. Machine learning-based stochastic task offloading algorithm in mobile-edge computing[J]. Journal of Beijing University of Posts and Telecommunications, 2019, 42(2):25-30.
14	Wu Y, Li P Q, Ni K J, et al. Delay-minimization nonorthogonal multiple access enabled multi-user mobile edge computation offloading[J]. IEEE Journal of Selected Topics in Signal Processing, 2019, 13(3): 392-407.
15	张志宏,刘传领.基于灰狼算法优化深度学习网络的网络流量预测[J].吉林大学学报:理学版,2021,59(3):619-626.
	Zhang Zhi-hong, Liu Chuan-ling. Grey wolf algorithm to optimize network traffic prediction of deep learning network[J]. Journal of Jilin University(Science Edition), 2021,59(3):619-626.
16	王彦琦,张强,朱刘涛,等.基于改进鲸鱼优化算法的GBDT回归预测模型[J].吉林大学学报:理学版,2022,60(2):401-408.
	Wang Yan-qi, Zhang Qiang, Zhu Liu-tao, et al. GBDT regression prediction model based on improved whale optimization algorithm[J]. Journal of Jilin University (Science Edition),2022,60(2):401-408.
17	赵鹏程,高尚,于洪梅.基于多智能体深度强化学习的空间众包任务分配[J].吉林大学学报:理学版,2022,60(2):321-331.
	Zhao Peng-cheng, Gao Shang, Yu Hong-mei. Spatial crowdsourcing task assignment based on multi-agent deep reinforcement learning[J]. Journal of Jilin University(Science Edition), 2022,60(2):321-331.
18	王宏志, 姜方达, 周明月. 基于遗传粒子群优化算法的认知无线电系统功率分配[J]. 吉林大学学报: 工学版, 2019, 49(4): 1363-1368.
	Wang Hong-zhi, Jiang Fang-da, Zhou Ming-yue. Power allocation of cognitive radio system based on genetic particle swarm optimization[J]. Journal of Jilin University(Engineering and Technology Edition), 2019, 49(4): 1363-1368.
19	王出航,王雪,胡黄水,等.基于改进GA和信任感知的无线传感器网络安全分簇路由协议[J].吉林大学学报:理学版,2021,59(5):1237-1244.
	Wang Chu-hang, Wang Xue, Hu Huang-shui, et al. Secure clustering routing protocol based on improved GA and trust-aware for wireless sensor networks[J]. Journal of Jilin University(Science Edition), 2021,59(5):1237-1244.
20	胡黄水,姚美琴,王亮,等.基于改进的AP和遗传算法的能量感知分簇路由协议[J].吉林大学学报:理学版,2021,59(6):1525-1531.
	Hu Huang-shui, Yao Mei-qin, Wang Liang, et al. Energy aware clustering routing protocol based on improved AP and genetic algorithm[J]. Journal of Jilin University(Science Edition), 2021,59(6):1525-1531.

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