边缘服务器计算资源分配方法与仿真实验

doi:10.13229/j.cnki.jdxbgxb.20231294

摘要/Abstract

摘要：

针对智慧渔业的数据采集传输过程中边缘服务器的带宽资源以及计算资源分配问题，通过求解时延最小化的带约束非线性方程，获得带宽以及计算资源的最优解。在计算资源为10 Gcycles/s、任务数量为40时，比较了粒子群算法、免疫算法、模拟退火算法、灰狼算法、萤火虫算法、序列二次规划算法等优化算法的效果，结果表明序列二次规划算法时延最小，为73.16 s。通过优先分配资源给时延与时延限制比小的任务改进算法，仿真实验结果显示：改进算法时延为56.91 s，较序列二次规划算法减小16.25 s，改进算法在带宽与计算资源分配优化问题中取得的效果更好。

关键词: 边缘计算, 资源分配, 时延限制, 序列二次规划, 智慧渔业

Abstract:

For the problem of allocating bandwidth resources as well as computational resources of edge servers during data collection and transmission in smart fishery， the optimal solutions of bandwidth as well as computational resources are obtained by solving the nonlinear equations with constraints for minimizing the time delay. The effect of optimization algorithms such as particle swarm algorithm， immune algorithm， simulated annealing algorithm， gray wolf algorithm， firefly algorithm， and sequential quadratic programming algorithm is compared when the computational resources are 10 Gcycles/s and the number of tasks is 40， and the sequential quadratic programming algorithm has the smallest latency of 73.16 s. By preferentially allocating resources to the task improvement algorithm with small latency to latency constraint ratio， the results show that the time delay of the improved algorithm is 56.91 s， which is 16.25 s less than the sequential quadratic programming algorithm， and the improved algorithm achieves better results in the problem of bandwidth and computational resource allocation optimization.

Key words: edge computing, resource allocation, latency constraints, sequential quadratic planning, smart fishery

中图分类号:

S951.2

黄汉英,李鹏飞. 边缘服务器计算资源分配方法与仿真实验[J]. 吉林大学学报(工学版), 2025, 55(1): 316-324.

Han-ying HUANG,Peng-fei LI. Method and experiments on edge computing resource allocation in smart fishery[J]. Journal of Jilin University(Engineering and Technology Edition), 2025, 55(1): 316-324.

图/表 7

图1

图2

表1

仿真参数"

参数	取值
无线信道带宽B	10 MHz
移动终端发射功率 $P i$	0.1 W
噪声功率 $N 0$	-113 dB·m
边缘服务器计算能力E	10 Gcycles/s
移动设备计算能力 $f l$	100 Mcycles/s
任务数据量 $D i$	［200，600］ kbits
边缘服务器功率P_e	750 W
时延限制T_lim	［0.8，1.2］ s
任务计算复杂度 $X i$	［1000，1200］ cycles/bit

表1

图3

图4

图5

表2

任务数量为40时各算法结果"

算法	各任务处理时延之和/s	超过时延限制任务数与总数比	时延与时延限制比 $θ$ 的平均值
SQP算法	73.16	1.00	1.85
改进算法1	56.91	0.625	1.41
改进算法2	57.09	0.525	1.40
改进算法3	73.35	1.00	1.85

表2

参考文献 31

1	施巍松, 孙辉, 曹杰, 等. 边缘计算:万物互联时代新型计算模型[J]. 计算机研究与发展, 2017, 54(5): 907-924.
	Shi Wei-song, Sun Hui, Cao Jie, et al. Edge computing: a new computing model in the internet age[J]. Computer Research and Development, 2017,54(5): 907-924.
2	陈祎鹏, 杨哲, 谷飞, 等. 一种基于博弈论的移动边缘计算资源分配策略[J]. 计算机科学, 2023, 50(2): 32-41.
	Chen Yi-peng, Yang Zhe, Gu Fei, et al. A resource allocation strategy for mobile edge computing based on game theory[J]. Computer Science, 2023,50(2): 32-41.
3	曾锋, 张政, 陈志刚. 基于深度强化学习的计算卸载与资源分配策略[J]. 通信学报, 2023, 44(7): 124-135.
	Zeng Feng, Zhang Zheng, Chen Zhi-gang. Computing offloading and resource allocation strategies based on deep reinforcement learning[J]. Journal of Communications, 2023,44(7): 124-135.
4	李晗, 孟顺梅, 蔡志成. 基于博弈论和粒子群优化的移动边缘计算任务卸载方法[J]. 应用科学学报, 2023, 41(3): 405-418.
	Li Han, Meng Shun-mei, Cai Zhi-cheng. Task unloading method of mobile edge computing based on game theory and particle swarm optimization[J]. Journal of Applied Sciences, 2023,41(3): 405-418.
5	雷雪梅, 刘丽, 王倩. 基于线性规划松弛的移动边缘计算卸载模型[J]. 计算机科学, 2023, 50(): 636-640.
	Lei Xue-mei, Liu Li, Wang Qian. Unloading model of mobile edge computing based on linear programming relaxation[J]. Computer Science, 2023,50(Sup.1): 636-640.
6	魏明亮, 耿绥燕, 赵雄文, 等. UDN中MEC的资源分配和任务卸载联合优化[J]. 北京邮电大学学报, 2023,46(2):50-56.
	Wei Ming-liang, Geng Sui-yan, Zhao Xiong-wen, et al. Joint optimization of resource allocation and task offloading for MEC in UDN[J]. Journal of Beijing University of Posts and Telecommunications, 2023,46(2): 50-56.
7	李云, 高倩, 姚枝秀, 等. 移动边缘计算中智能服务编排和算网资源分配联合优化方法[J]. 通信学报, 2023, 44(7): 51-63.
	Li Yun, Gao Qian, Yao Zhi-xiu, et al. Joint optimization method of intelligent service layout and computing network resource allocation in mobile edge computing [J]. Journal of Communications, 2023,44(7): 51-63.
8	Kuang Z F, Ma Z H, Li Z, et al. Cooperative computation offloading and resource allocation for delay minimization in mobile edge computing[J]. Journal of Systems Architecture, 2021,118: No.102167.
9	崔高峰, 徐媛媛, 张尚宏, 等. 基于最小能耗的多无人机无线网络安全数据卸载策略[J]. 通信学报, 2021, 42(5): 51-62.
	Cui Gao-feng, Xu Yuan-yuan, Zhang Shang-hong, et al. A secure data offloading strategy for multi drone wireless networks based on minimum energy consumption[J]. Journal of Communications, 2021,42(5): 51-62.
10	Jayanetti A, Halgamuge S, Buyya R. Deep reinforcement learning for energy and time optimized scheduling of precedence-constrained tasks in edge-cloud computing environments[J]. Future Generation Computer Systems, 2022, 137: 14-30.
11	Lai P, He Q, Cui G M, et al. QoE-aware user allocation in edge computing systems with dynamic QoS[J]. Future Generation Computer Systems, 2020,112:684-694.
12	Chakraborty S, Mazumdar K. Sustainable task offloading decision using genetic algorithm in sensor mobile edge computing[J]. Journal of King Saud University―Computer and Information Sciences, 2022, 34(4):1552-1568.
13	Alqarni M A, Mousa M H, Hussein M K. Task offloading using GPU-based particle swarm optimization for high-performance vehicular edge computing[J]. Journal of King Saud University—Computer and Information Sciences, 2022, 34(10): 10356-10364.
14	Ju X, Su SC, Xu C J, et al.Computation offloading and tasks scheduling for the internet of vehicles in edge computing: a deep reinforcement learning-based pointer network approach[J]. Computer Networks, 2023, 223: No.109572.
15	Zhang C, Zhou G H, Li J J, et al. A multi-access edge computing enabled framework for the construction of a knowledge-sharing intelligent machine tool swarm in Industry 4.0[J]. Journal of Manufacturing Systems, 2023, 66: 56-70.
16	尉健一, 吴菁晶. 基于边缘计算的工业物联网中资源分配算法[J].东北大学学报:自然科学版, 2023, 44(8): 1072-1077, 1110.
	Wei Jian-yi, Wu Jing-jing. Resource allocation algorithm in industrial IoT based on edge computing[J]. Journal of Northeast University(Natural Science Edition), 2023,44(8): 1072-1077, 1110.
17	赵羽, 杨洁, 刘淼, 等. 面向视频监控基于联邦学习的智能边缘计算技术[J]. 通信学报, 2020, 41(10):109-115.
	Zhao Yu, Yang Jie, Liu Miao, et al. Intelligent edge computing technology based on federated learning for video surveillance[J]. Journal of Communications, 2020,41(10): 109-115.
18	期治博, 杜磊, 霍如, 等. 基于边缘计算的多摄像头视频协同分析方法[J]. 通信学报, 2023, 44(8): 14-26.
	Qi Zhi-bo, Du Lei, Huo Ru, et al. Multi camera video collaborative analysis method based on edge computing[J]. Journal of Communications, 2023,44(8): 14-26.
19	薛建彬, 安悦, 关向瑞, 等. 车载多接入边缘网络中联合资源分配和动态任务卸载方案[J]. 国防科技大学学报, 2022, 44(6): 61-69.
	Xue Jian-bin, An Yue, Guan Xiang-rui, et al. Joint resource allocation and dynamic task offloading scheme in vehicular multi access edge networks[J]. Journal of National University of Defense Science and Technology, 2022,44(6): 61-69.
20	王练, 闫润搏, 徐静. 车载边缘计算中多任务部分卸载方案研究[J]. 电子与信息学报, 2023, 45(3): 1094-1101.
	Wang Lian, Yan Run-bo, Xu Jing. Research on multi task partial unloading scheme in on-board edge computing[J]. Journal of Electronics and Information, 2023,45(3): 1094-1101.
21	Liu S, Tian J, Zhai C, et al. Joint computation offloading and resource allocation in vehicular edge computing networks[J]. Digital Communications and Networks,2023,9(6):1399-1410.
22	Xu D H, Xu D.Cooperative task offloading and resource allocation for UAV-enabled mobile edge computing systems[J]. Computer Networks,2023, 223:No.109574.
23	刘兴光, 周力, 张晓瀛, 等. 基于边缘智能感知的无人机空间航迹规划方法[J]. 计算机科学, 2023, 50(9): 311-317.
	Liu Xing-guang, Zhou Li, Zhang Xiao-ying, et al. A method for unmanned aerial vehicle spatial trajectory planning based on edge intelligent perception[J]. Computer Science, 2023,50(9): 311-317.
24	姜泽峰, 曹润宇, 张善新. MEC网络中多无人机协同优化计算卸载策略[J]. 传感器与微系统, 2023, 42(7): 52-56.
	Jiang Ze-feng, Cao Run-yu, Zhang Shan-xin. Collaborative optimization and offloading strategy for multiple unmanned aerial vehicles in MEC networks[J]. Sensors and Microsystems, 2023,42(7): 52-56.
25	陈阳, 皮德常, 代成龙, 等. 多无人机协同陆地设施辅助移动边缘计算的系统能耗最小化方法[J]. 电子学报, 2023, 51(4): 984-992.
	Chen Yang, Pi De-chang, Dai Cheng-long, et al. System energy consumption minimization method for mobile edge computing assisted by multi UAV cooperative land facilities[J]. Journal of Electronics, 2023,51(4): 984-992.
26	李斌, 杨蓉蓉. 无人机辅助反向散射通信计算任务卸载与资源分配[J]. 电子与信息学报, 2023,45(7): 2334-2341.
	Li Bin, Yang Rong-rong. Unmanned aerial vehicle assisted backscatter communication computing task unloading and resource allocation[J]. Journal of Electronics and Information Technology, 2023, 45(7): 2334-2341.
27	夏雪, 柴秀娟, 张凝, 等. 用于边缘计算设备的果树挂果量轻量化估测模型[J]. 智慧农业, 2023,5(2): 1-12.
	Xia Xue, Chai Xiu-juan, Zhang Ning, et al. A lightweight estimation model of fruit bearing capacity of fruit trees for edge computing equipment[J]. Smart Agriculture, 2023,5(2): 1-12.
28	牛恺锐, 张正华, 包飞霞, 等. 基于边缘计算的作物病虫害监测嵌入式系统设计[J]. 计算机与网络,2021, 47(14): 61-65.
	Niu Kai-rui, Zhang Zheng-hua, Bao Fei-xia, et al. Embedded system design of crop pest monitoring based on edge computing[J]. Computer and Network, 2021,47(14): 61-65.
29	查文文, 潘伟豪, 陈成鹏, 等. 基于边缘计算与改进YOLOv5的群养生猪姿态识别及跟踪研究[J]. 东北农业大学学报, 2023, 54(3): 83-96.
	Zha Wen-wen, Pan Wei-hao, Chen Cheng-peng, et al. Study on pose recognition and tracking of herd-raised pigs based on edge computing and improved YOLOv5[J]. Journal of Northeast Agricultural University, 2023, 54(3): 83-96.
30	孙传恒, 袁晟, 罗娜, 等. 基于区块链和边缘计算的水稻原产地溯源方法研究[J]. 农业机械学报, 2023, 54(5): 359-368.
	Sun Chuan-heng, Yuan Sheng, Luo Na, et al. Research on the origin traceability method of rice based on blockchain and edge computing[J]. Journal of Agricultural Machinery, 2023,54(5): 359-368.
31	杨志军, 寇倩兰, 丁洪伟. 基于限定服务的双服务器轮询控制性能研究[J]. 计算机仿真, 2022, 39(8): 466-472.
	Yang Zhi-jun, Kou Qian-lan, Ding Hong-wei. Research on performance of dual-server polling control performance based on limited service[J].Computer Simulation,2022,39(8):466-472.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

[1]	杨楠,肖军. 序列二次规划算法下城市智能交通运行节能优化控制[J]. 吉林大学学报(工学版), 2024, 54(8): 2223-2228.
[2]	朱思峰,蔡江昊,柴争义,孙恩林. 车联网边缘场景下基于免疫算法的计算卸载优化[J]. 吉林大学学报(工学版), 2024, 54(1): 221-231.
[3]	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.
[4]	李丽娜,魏晓辉,郝琳琳,王兴旺,王储. 大规模流数据处理中代价有效的弹性资源分配策略[J]. 吉林大学学报(工学版), 2020, 50(5): 1832-1843.
[5]	刘毅,肖玲玲,王改静,张武军. 基于联合优化的D2D资源分配算法[J]. 吉林大学学报(工学版), 2020, 50(1): 306-314.
[6]	姜来为, 沙学军, 吴宣利, 张乃通. LTE-A异构网络中新的用户选择接入和资源分配联合方法[J]. 吉林大学学报(工学版), 2017, 47(6): 1926-1932.
[7]	单泽彪, 石要武, 刘小松, 李新波. 时变遗忘因子动态DOA跟踪算法[J]. 吉林大学学报(工学版), 2016, 46(2): 632-638.
[8]	赵晓晖，杨伟伟，金晓光. 基于不同时延业务的中继正交频分复用系统资源分配算法[J]. 吉林大学学报(工学版), 2015, 45(6): 2049-2055.
[9]	唐瑞春, 邱悦, 丁香乾, 李静. 基于效用最大化协商机制的云媒体资源分配算法[J]. 吉林大学学报(工学版), 2015, 45(3): 932-937.
[10]	管成，王飞，张登雨. 基于NURBS的挖掘机器人时间最优轨迹规划[J]. 吉林大学学报(工学版), 2015, 45(2): 540-546.
[11]	陈健, 樊光辉, 阔永红. 认知无线电中继协助网络资源分层优化算法[J]. 吉林大学学报(工学版), 2014, 44(5): 1498-1505.
[12]	游晓明, 刘升, 王裕明. 量子行为网络资源并行分配优化模型及其应用[J]. 吉林大学学报(工学版), 2012, 42(增刊1): 341-345.
[13]	丛犁, 张海林, 刘毅, 赵力强, 张国鹏. 基于粒子群优化的协作网络资源分配的博弈策略[J]. 吉林大学学报(工学版), 2012, 42(01): 207-212.
[14]	程翔, 李立. 单物品多单元双向拍卖环境下的网格资源分配仿真[J]. 吉林大学学报(工学版), 2010, 40(05): 1359-1365.
[15]	卢前溪,彭涛,王玮,王文博. 认知无线电网络上行链路子载波和功率分配[J]. 吉林大学学报(工学版), 2010, 40(04): 1144-1149.