车联网边缘计算场景下基于改进型NSGA-Ⅱ算法的边缘服务器部署决策

朱思峰; 王钰; 陈昊; 朱海; 柴争义; 杨诚瑞

doi:10.11959/j.issn.2096-3750.2024.00382

您当前的位置：

首页 >

文章列表页 >

车联网边缘计算场景下基于改进型NSGA-Ⅱ算法的边缘服务器部署决策

理论与技术 | 更新时间：2024-06-05

- 车联网边缘计算场景下基于改进型NSGA-Ⅱ算法的边缘服务器部署决策
- Edge server deployment decision based on improved NSGA-Ⅱ in the Internet of vehicles edge computing scenario
- 物联网学报 2024年8卷第1期页码：84-97
- 作者机构：
  
  1. 天津城建大学计算机与信息工程学院，天津 300384
  2. 河南工程学院计算机学院，河南郑州 451191
  3. 天津工业大学计算机科学与技术学院，天津 300387
- 作者简介：
  
  [ "朱思峰（1975- ），男，博士，天津城建大学计算机与信息工程学院教授，主要研究方向为车联网、移动边缘计算和多目标优化算法等" ]
  [ "王钰（1996- ），男，天津城建大学计算机与信息工程学院硕士生，主要研究方向为车联网、边缘计算和多目标优化算法" ]
  [ "陈昊（1979- ），男，博士，天津城建大学计算机与信息工程学院副教授，主要研究方向为无线通信、通信系统接口、功率控制等" ]
  [ "朱海（1977- ），男，博士，河南工程学院计算机学院教授，主要研究方向为车联网、移动边缘计算和多目标优化算法等" ]
  [ "柴争义（1976- ），男，博士，天津工业大学计算机科学与技术学院教授，主要研究方向为车联网、移动边缘计算和多目标优化算法等" ]
  [ "杨诚瑞（1996- ），男，天津城建大学计算机与信息工程学院硕士生，主要研究方向为车联网、移动边缘计算和多目标优化算法等" ]
- 基金信息：
  
  国家自然科学基金项目;The National Natural Science Foundation of China(62172457);天津市自然科学基金重点项目;Key projects of Tianjin Natural Science Foundation(22JCZDJC00600)
- DOI：10.11959/j.issn.2096-3750.2024.00382
  中图分类号： TP391
- 纸质出版日期：2024-03-30，
  
  网络出版日期：2024-03，
- 稿件说明：
移动端阅览
朱思峰, 王钰, 陈昊, 等. 车联网边缘计算场景下基于改进型NSGA-Ⅱ算法的边缘服务器部署决策[J]. 物联网学报, 2024,8(1):84-97.

SIFENG ZHU, YU WANG, HAO CHEN, et al. Edge server deployment decision based on improved NSGA-Ⅱ in the Internet of vehicles edge computing scenario. [J]. Chinese journal on internet of things, 2024, 8(1): 84-97.
朱思峰, 王钰, 陈昊, 等. 车联网边缘计算场景下基于改进型NSGA-Ⅱ算法的边缘服务器部署决策[J]. 物联网学报, 2024,8(1):84-97. DOI： 10.11959/j.issn.2096-3750.2024.00382.

SIFENG ZHU, YU WANG, HAO CHEN, et al. Edge server deployment decision based on improved NSGA-Ⅱ in the Internet of vehicles edge computing scenario. [J]. Chinese journal on internet of things, 2024, 8(1): 84-97. DOI： 10.11959/j.issn.2096-3750.2024.00382.

摘要

车联网环境下，边缘服务器的放置位置与部署数量直接影响到边缘计算的效率。由于在宏基站或基站上部署大型边缘服务器的成本较高，可以在微基站上部署一个小型边缘服务器作为补充，并通过优化大型边缘服务器的放置位置来降低成本。为了最小化边缘服务器的部署代价和服务延迟、最大化运营商的收入和服务器负载均衡度，把边缘服务器放置问题与车联网用户应用服务放置问题联合建模为一个多目标优化问题，并提出了基于改进型NSGA-Ⅱ算法的放置方案。实验结果表明，提出的边缘服务器放置方案能够降低约44%的边缘服务器部署成本，降低约14.2%的时延，提升24.2%的运营商收入，具有较好的应用价值。

Abstract

In the context of the Internet of vehicles

the placement and deployment number of edge servers directly affect the efficiency of edge computing.Due to the high cost of deploying a large edge server on a macro base station and a base station

it can be complemented by deploying a small edge server on a micro base station

and the cost reduction needs to be optimized by optimizing the placement of large edge servers.In order to minimize the deployment cost and service delay of the edge server

and maximize the operator’s revenue and server load balance

the edge server placement problem combined with the vehicle networking user application service was modeled as a multi-objective optimization problem and a placement scheme based on improved NSGA-Ⅱ algorithm was proposed.The experimental results show that the proposed scheme can reduce the deployment cost of edge servers by about 44%

the latency by about 14.2%

and improve the revenue of operators by 24.2%

which has good application value.

关键词

车联网边缘计算边缘服务器部署问题多目标优化算法NSGA-Ⅱ

Keywords

Internet of vehiclesedge computingedge server placement problemmulti-objective optimization algorithmNSGA-Ⅱ

references

张依琳, 梁玉珠, 尹沐君 ,等. 移动边缘计算中计算卸载方案研究综述[J]. 计算机学报, 2021,44(12): 2406-2430.

ZHANG Y L, LIANG Y Z, YIN M J ,et al. Survey on the methods of computation offloading in mobile edge computing[J]. Chinese Journal of Computers, 2021,44(12): 2406-2430.

ZHANG L X, ZHOU L Q, SALAH A . Efficient scientific workflow scheduling for deadline-constrained parallel tasks in cloud computing environments[J]. Information Sciences, 2020,531: 31-46.

MAO Y Y, ZHANG J, LETAIEF K B . Dynamic computation offloading for mobile-edge computing with energy harvesting devices[J]. IEEE Journal on Selected Areas in Communications, 2016,34(12): 3590-3605.

GE X H, TU S, MAO G Q ,et al. 5G ultra-dense cellular networks[J]. IEEE Wireless Communications, 2016,23(1): 72-79.

SHEN B W, XU X L, QI L Y ,et al. Dynamic server placement in edge computing toward Internet of Vehicles[J]. Computer Communications, 2021,178: 114-123.

ZHAO X H, SHI Y, CHEN S Z . MAESP:mobility aware edge service placement in mobile edge networks[J]. Computer Networks, 2020,182:107435.

OUYANG T, RUI L, XU C ,et al. Adaptive user-managed service placement for mobile edge computing:an online learning approach[C]// Proceedings of the IEEE INFOCOM 2019-IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2019: 1468-1476.

XU J, CHEN L X, ZHOU P . Joint service caching and task offloading for mobile edge computing in dense networks[C]// Proceedings of the IEEE INFOCOM 2018-IEEE Conference on Computer Communications. Piscataway:IEEE Press, 2018: 207-215.

TRAN T X, CHAN K, POMPILI D . COSTA:cost-aware service caching and task offloading assignment in mobile-edge computing[C]// Proceedings of the 2019 16th Annual IEEE International Conference on Sensing,Communication,and Networking (SECON). Piscataway:IEEE Press, 2019: 1-9.

HE T, KHAMFROUSH H, WANG S Q ,et al. It’s hard to share:joint service placement and request scheduling in edge clouds with sharable and non-sharable resources[C]// Proceedings of the 2018 IEEE 38th International Conference on Distributed Computing Systems (ICDCS). Piscataway:IEEE Press, 2018: 365-375.

SANTOS F , IMMICH R , MADEIRA E R M . Multimedia services placement algorithm for cloud-fog hierarchical environments[J]. Computer communications, 2022,191: 78-91.

LI C L, SONG M Y, YU C C ,et al. Mobility and marginal gain based content caching and placement for cooperative edge-cloud computing[J]. Information Sciences, 2021,548: 153-176.

韩牟, 杨晨, 华蕾 ,等. 面向移动边缘计算车联网中车辆假名管理方案[J]. 计算机研究与发展, 2022,59(4): 781-795.

HAN M, YANG C, HUA L ,et al. Vehicle pseudonym management scheme in Internet of vehicles for mobile edge computing[J]. Journal of Computer Research and Development, 2022,59(4): 781-795.

张珠君, 范伟, 朱大立 . 面向智能家居的区块链轻量级认证机制[J]. 软件学报, 2022,33(7): 2699-2715.

ZHANG Z J, FAN W, ZHU D L . Lightweight blockchain authentication mechanism for smart home[J]. Journal of Software, 2022,33(7): 2699-2715.

LUO Y Z, DING W R, ZHANG B C . Optimization of task scheduling and dynamic service strategy for multi-UAV-enabled mobileedge computing system[J]. IEEE Transactions on Cognitive Communications and Networking, 2021,7(3): 970-984.

HADŽIĆ I, ABE Y, WOITHE H C . Server placement and selection for edge computing in the ePC[J]. IEEE Transactions on Services Computing, 2019,12(5): 671-684.

JIA M K, CAO J N, LIANG W F . Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks[J]. IEEE Transactions on Cloud Computing, 2017,5(4): 725-737.

FAN Q, ANSARI N . On cost aware cloudlet placement for mobile edge computing[J]. IEEE/CAA Journal of Automatica Sinica, 2019,6(4): 926-937.

WANG S G, ZHAO Y L, XU J ,et al. Edge server placement in mobile edge computing[J]. Journal of Parallel and Distributed Computing, 2019,127: 160-168.

CAO K, LI L Y, CUI Y G ,et al. Exploring placement of heterogeneous edge servers for response time minimization in mobile edgecloud computing[J]. IEEE Transactions on Industrial Informatics, 2021,17(1): 494-503.

WANG Z M, ZHANG W Y, JIN X M ,et al. An optimal edge server placement approach for cost reduction and load balancing in intelligent manufacturing[J]. The Journal of Supercomputing, 2022,78(3): 4032-4056.

ZHAO X B, ZENG Y, DING H W ,et al. Optimize the placement of edge server between workload balancing and system delay in smart city[J]. Peer-to-Peer Networking and Applications, 2021,14(6): 3778-3792.

MAIA A M, GHAMRI-DOUDANE Y, VIEIRA D ,et al. An improved multi-objective genetic algorithm with heuristic initialization for service placement and load distribution in edge computing[J]. Computer networks, 2021,194(20): 108146.1-108146.15.

GAO B, ZHOU Z, LIU F M ,et al. An online framework for joint network selection and service placement in mobile edge computing[J]. IEEE Transactions on Mobile Computing, 2022,21(11): 3836-3851.

ZHANG Z H, WU G W, REN H Z . Multi-attribute-based QoSaware virtual network function placement and service chaining algorithms in smart cities[J]. Computers ＆ Electrical Engineering, 2021,96:107465.

HENG L, YIN G F, ZHAO X F . Energy aware cloud-edge service placement approaches in the Internet of Things communications[J]. International Journal of Communication Systems, 2022,35(1): e4899.1-e4899.23.

YUAN B B, GUO S T, WANG Q Y . Joint service placement and request routing in mobile edge computing[J]. Ad Hoc Networks, 2021,120:102543.

WANG Y M, ZHAO C, YANG S S ,et al. MPCSM:microservice placement for edge-cloud collaborative smart manufacturing[J]. IEEE Transactions on Industrial Informatics, 2021,17(9): 5898-5908.

TALPUR A, GURUSAMY M . DRLD-SP:a deep-reinforcementlearning-based dynamic service placement in edge-enabled Internet of vehicles[J]. IEEE Internet of Things Journal, 2022,9(8): 6239-6251.

SALAHT F A, DESPREZ F, LEBRE A . An overview of service placement problem in fog and edge computing[J]. ACM Computing Surveys, 53(3): 65.

ZHANG X L, LI Z J, LAI C ,et al. Joint edge server placement and service placement in mobile-edge computing[J]. IEEE Internet of Things Journal, 2022,9(13): 11261-11274.

LÄHDERANTA T, LEPPÄNEN T, RUHA L ,et al. Edge computing server placement with capacitated location allocation[J]. Journal of Parallel and Distributed Computing, 2021,153: 130-149.

黄景源 . 上海5G基站建设密度全国排名第一，将持续推进700 MHz频段补充完善5G网络覆盖[J]. 界面新闻, 2022.

HUANG J Y . Shanghai’s 5G base station construction density ranks first in the country,and will continue to promote the 700 MHz frequency band to supplement and improve the 5G network coverage[J]. Jiemian News, 2022.

TIAN R L, WANG Y L . Optimal strategies and pricing analysis in M/M/1 queues with a single working vacation and multiple vacations[J]. RAIRO-Operations Research, 2020,54(6): 1593-1612.

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

面向情感计算的智慧教室系统构建以及优化

基于能量收集技术的协作卸载计算方案

基于STM32的农业物联网病虫害图像识别算法研究

一种应用于边缘计算的区块链分片方案

车联网边缘智能：概念、架构、问题、实施和展望