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

Abstract

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

CLC Number:

S951.2

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.

Figures/Tables 7

Fig.1

Fig.2

Table 1

Experimental parameters"

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

Table 1

Fig.3

Fig.4

Fig.5

Table 2

Results of each algorithm when numberof tasks is 40"

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

Table 2

References 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.

Related Articles 15

[1]	Si-feng ZHU,Jiang-hao CAI,Zheng-yi CHAI,En-lin SUN. Computing offloading optimization scheme based on immune algorithm in edge computing scenes of internet of vehicles [J]. Journal of Jilin University(Engineering and Technology Edition), 2024, 54(1): 221-231.
[2]	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.
[3]	Li-na LI,Xiao-hui WEI,Lin-lin HAO,Xing-wang WANG,Chu WANG. Cost⁃effective elastic resource allocation strategy in large⁃scale streaming data processing [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(5): 1832-1843.
[4]	Yi LIU,Ling-ling XIAO,Gai-jing WANG,Wu-jun ZHANG. Resource allocation algorithm based joint optimization for D2D communications in cellular networks [J]. Journal of Jilin University(Engineering and Technology Edition), 2020, 50(1): 306-314.
[5]	JIANG Lai-wei, SHA Xue-jun, WU Xuan-li, ZHANG Nai-tong. Novel joint user association and resource allocation method in LTE-A HetNets [J]. 吉林大学学报(工学版), 2017, 47(6): 1926-1932.
[6]	ZHAO Xiao-hui, YANG Wei-wei, JIN Xiao-guang. Resource allocation algorithm for different delay traffic in relay-based OFDM systems [J]. 吉林大学学报(工学版), 2015, 45(6): 2049-2055.
[7]	TANG Rui-chun, QIU Yue, DING Xiang-qian, LI Jing. Cloud media resource allocation algorithm based on utility maximization negotiation [J]. 吉林大学学报(工学版), 2015, 45(3): 932-937.
[8]	CHEN Jian,FAN Guang-hui,KUO Yong-hong. Hierarchical optimization algorithm for resource allocation in relay-assisted cognitive radio network [J]. 吉林大学学报(工学版), 2014, 44(5): 1498-1505.
[9]	YOU Xiao-ming, LIU Sheng, WANG Yu-ming. Quantum-behaved network resource parallel allocation optimization model and application [J]. 吉林大学学报(工学版), 2012, 42(增刊1): 341-345.
[10]	CONG Li, ZHANG Hai-lin, LIU Yi, ZHAO Li-qiang, ZHANG Guo-peng. Particle swarm optimized game theory for resource allocation in cooperative networks [J]. 吉林大学学报(工学版), 2012, 42(01): 207-212.
[11]	CHENG Xiang, LI Li. Simulation of grid allocation in singleitem multiunit double auction market [J]. 吉林大学学报(工学版), 2010, 40(05): 1359-1365.
[12]	LU Qian-xi,PENG Tao,WANG Wei,WANG Wen-bo. Subcarrier and power allocation algorithm in uplink of cognitive radio networks [J]. 吉林大学学报(工学版), 2010, 40(04): 1144-1149.
[13]	ZHANG Huan, WANG Wen-Bo, GAO Wei-Dong, BENG Mu-Gen. Resource allocation for multihop relay links with adaptive frame structure [J]. 吉林大学学报(工学版), 2010, 40(02): 571-0575.
[14]	LIU Xue-jie, LIU Yan-heng, LI Mu-shan, Li Yan,Guan Jian. Dynamic resource allocation of extended policy based network management(PBNM) model in mobile environment [J]. 吉林大学学报(工学版), 2010, 40(01): 176-0182.
[15]	MEI Fang，LIU Yan-heng，ZHANG Xu-li，Gu Tian-ye，WANG Wang . Dynamic conflict resolution mechanism for resource management policy in mobile network [J]. 吉林大学学报(工学版), 2009, 39(02): 430-0435.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed

Comments

Recommended 0

No Suggested Reading articles found!

Method and experiments on edge computing resource allocation in smart fishery

RICH HTML

PDF (PC)