计算机科学

计算机科学 ›› 2025, Vol. 52 ›› Issue (11): 306-319.doi: 10.11896/jsjkx.240900004

• 计算机网络 • 上一篇    下一篇

灾害场景下基于SDN和无人机辅助的无线边缘存储系统性能优化方法

孙石泉1, 叶苗1, 朱成2,3, 王勇3, 蒋秋香4   

  1. 1 桂林电子科技大学信息与通信学院 广西 桂林 541004
    2 桂林医科大学信息中心 广西 桂林 541001
    3 桂林电子科技大学计算机与信息安全学院 广西 桂林 541004
    4 桂林电子科技大学光电工程学院 广西 桂林 541004
  • 收稿日期:2024-09-02 修回日期:2024-12-14 出版日期:2025-11-15 发布日期:2025-11-06
  • 通讯作者: 朱成(zhucheng@glmu.edu.cn)
  • 作者简介:(sunshiquan.stu@foxmail.com)
  • 基金资助:
    国家自然科学基金(62161006,62172095);广西研究生教育创新计划(YCSW2023310);广西无线宽带通信与信号处理重点实验室主任基金项目(GXKL06220110)

Performance Optimization of Wireless Edge Storage System Based on SDN and Drone Assistance in Disaster Scenarios

SUN Shiquan1, YE Miao1, ZHU Cheng2,3, WANG Yong3, JIANG Qiuxiang4   

  1. 1 School of Information and Communications,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
    2 Network Information Center,Guilin Medical University,Guilin,Guangxi 541001,China
    3 School of Computer Science and Information Security,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
    4 School of Optoelectronic Engineering,Guilin University of Electronic Technology,Guilin,Guangxi 541004,China
  • Received:2024-09-02 Revised:2024-12-14 Online:2025-11-15 Published:2025-11-06
  • About author:SUN Shiquan,born in 1999,postgra-duate.His main research interests include software-defined networking and embedded systems.
    ZHU Cheng,born in 1973,professor,master's supervisor.His main research interests include artificial intelligence and big data, network security management,and smart library construction.
  • Supported by:
    National Natural Science Foundation of China(62161006,62172095),Subsidization of Innovation Project of Guangxi Graduate Education(YCSW2023310) and Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing(GXKL06220110).

PDF (PC)

28

摘要： 传统边缘分布式存储系统中网络配置繁琐,优化网络所需的网络状态信息测量操作开销大,当终端设备对数据存储和检索的业务需求处于高峰时,会导致网络链路负载过重从而影响数据转发传输的性能。此外,现有分布式存储系统在进行数据的存储节点选择时,只考虑了节点剩余存储空间,没有考虑网络状态和节点自身负载对系统存储性能的影响。为解决上述问题,设计和实现了一种基于软件定义网络(Software Defined Network,SDN)和无人机辅助的边缘分布式存储系统,利用SDN技术测量网络状态、网络节点自身负载和存储节点负载状态信息,通过无人机移动节点飞行到重负载网络节点的上方进行分流以平衡各条链路的流量负载;对于重负载网络节点和存储节点的选择,提出了一种基于多属性决策模型综合考虑网络状态和节点自身负载状态的节点选择算法,选择出重负载网络节点和合适的存储节点,然后通过对无人机的位置部署,实现网络链路流量的分流,平衡网络链路的流量负载。经实验测试,结果显示在无线Mesh网络拓扑中,所提无线边缘分布式存储系统的存储性能优于现有边缘分布式存储系统,存储时间明显缩短,在增加流量负载的情况下依然可以保持良好的存储性能,具有良好的负载均衡性能。

关键词: 边缘分布式存储, 软件定义网络, 节点选择, 负载均衡, 无线Mesh

Abstract: Traditional edge distributed storage systems often suffer from cumbersome network configuration and high operational overhead in measuring network state information.During peak demand periods for data storage and retrieval by terminal devices,network links can become overloaded,adversely affecting data transmission performance.Furthermore,existing distributed sto-rage systems typically consider only the remaining storage space of nodes when selecting storage nodes,neglecting the impact of network state and node load on system storage performance.To address these issues,this paper designs and implements an edge-distributed storage system assisted by software-defined network(SDN) and drones.The system uses SDN technology to measure network state,node load,and storage node load information.Drones fly above heavily loaded network nodes to offload traffic and balance the load across different links.For the selection of heavily loaded network nodes and storage nodes,this paper proposes a node selection algorithm based on a multi-attribute decision model that comprehensively considers network state and node load.The algorithm identifies heavily loaded network nodes and suitable storage nodes,and deployment of drones helps achieve traffic offloading and load balancing.Experimental tests on a wireless Mesh network topology demonstrate that the proposed wireless edge-distributed storage system outperforms existing edge-distributed storage systems in terms of storage performance.The proposed system significantly reduces storage time and maintains good performance even under increased traffic load,demonstrating excellent load-balancing capabilities.

Key words: Edge distributed storage, Software defined network, Node selection, Load balancing, Wireless Mesh

中图分类号: 

  • TP393
[1]HUDA N U,AHMED I,ADNAN M,et al.Experts and intelligent systems for smart homes' Transformation to Sustainable Smart Cities:A comprehensive review[J].Expert Systems with Applications,2024,238:122380.
[2]MALIK H,ANEES T,FAHEEM M,et al.Blockchain and Internet of Things in smart cities and drug supply management:Open issues,opportunities,and future directions[J].Internet of Things,2023,23:100860.
[3]CHAWLA D,MEHRA P S.A roadmap from classical cryptography to post-quantum resistant cryptography for 5G-enabled IoT:Challenges,opportunities and solutions[J].Internet of Things,2023,24:100950.
[4]The big picture on the internet of things and the smart city:a review of what we know and what we need to know-ScienceDirect[EB/OL].https://www.sciencedirect.com/science/article/pii/S2542660522000609.
[5]HAZRA A,RANA P,ADHIKARI M,et al.Fog computing for next-generation Internet of Things:Fundamental,state-of-the-art and research challenges[J].Computer Science Review,2023,48:100549.
[6]FENG C,HAN P,ZHANG X,et al.Computation offloading in mobile edge computing networks:A survey[J].Journal of Network and Computer Applications,2022,202:103366.
[7]An application of meta-heuristic and nature-inspired algorithms for designing reliable networks based on the Internet of things:A systematic literature review-Gong-2023-International Journal of Communication Systems-Wiley Online Library[EB/OL].https://onlinelibrary.wiley.com/doi/abs/10.1002/dac.5416.
[8]CHAI Y,ZENG X J,LIU Z.The future of wireless mesh network in next-generation communication:a perspective overview[J].Evolving Systems,2024,15(4):1635-1648.
[9]NARWARIA A,MAZUMDAR A P.Software-Defined Wireless Sensor Network:A Comprehensive Survey[J].Journal of Network and Computer Applications,2023,215:103636.
[10]Symmetry Dynamic Load Balancing Techniques in the IoT:A Review[EB/OL].https://www.mdpi.com/2073-8994/14/12/2554.
[11]SONG Z,QIN X,HAO Y,et al.A comprehensive survey on aerial mobile edge computing:Challenges,state-of-the-art,and future directions[J].Computer Communications,2022,191:233-256.
[12]A Survey on UAV-Enabled Edge Computing:Resource Management Perspective [EB/OL].https://dl.acm.org/doi/full/10.1145/3626566.
[13]ALSUHLI G,FAHIM A,GADALLAH Y.A survey on the role of UAVs in the communication process:A technological perspective[J].Computer Communications,2022,194:86-123.
[14]ADNAN M H,ZUKARNAIN Z A,AMODU O A.Fundamental design aspects of UAV-enabled MEC systems:A review on models,challenges,and future opportunities[J].Computer Science Review,2024,51:100615.
[15]GU X,ZHANG G.A survey on UAV-assisted wireless communications:Recent advances and future trends[J].Computer Communications,2023,208:44-78.
[16]A Storage Resource Collaboration Model Among Edge Nodes in Edge Federation Service [EB/OL].https://ieeexplore.ieee.org/abstract/document/9786648.
[17]ISYAKU B,BAKAR K,YUSUF N M,et al.Software defined wireless sensor load balancing routing for internet of things applications:Review of approaches[J].Heliyon,Elsevier,2024,10(9):e29965.
[18]RASHID M T,ZHANG D,WANG D.EdgeStore:Towards an Edge-Based Distributed Storage System for Emergency Response [C]//2019 IEEE 21st International Conference on High Performance Computing and Communications; IEEE 17th International Conference on Smart City;IEEE 5th International Conference on Data Science and Systems.2019:2543-2550.
[19]BHOWMIK C D,GAYEN T.Traffic aware dynamic load distribution in the Data Plane of SDN using Genetic Algorithm:A case study on NSF network[J].Pervasive and Mobile Computing,2023,88:101723.
[20]SHA Z,HUO R,SUN C,et al.A Task-Oriented Hybrid Routing Approach based on Deep Deterministic Policy Gradient[J].Computer Communications,2023,210:183-193.
[21]PEI X,SUN P,HU Y,et al.Enabling efficient routing for traffic engineering in SDN with Deep Reinforcement Learning[J].Computer Networks,2024,241:110220.
[22]ZHENG H,GUO J,ZHOU Q,et al.Application of improved ant colony algorithm in load balancing of software-defined networks[J].The Journal of Supercomputing,2023,79(7):7438-7460.
[23]YOUNUS M U,KHAN M K,BHATTI A R.Improving the Software-Defined Wireless Sensor Networks Routing Performance Using Reinforcement Learning[J].IEEE Internet of Things Journal,2022,9(5):3495-3508.
[24]RIYAZ B M,ALI F,ALANSARI Z,et al.Artificial Intelligence Based Reliable Load Balancing Framework in Software-Defined Networks[J].Computers,Materials and Continua,2021,70:251-256.
[25]ILBPS:An Integrated Optimization Approach Based on Adap-tive Load-Balancing and Heuristic Path Selection in SDN[EB/OL].https://ieeexplore.ieee.org/abstract/document/10233939.
[26]Electronics Research on Generalized Intelligent Routing Technology Based on Graph Neural Network[EB/OL].https://www.mdpi.com/2079-9292/11/18/2952.
[27]GUNAVATHIE M A,UMAMAHESWARI S.MLPRS:A Machine Learning-Based Proactive Re-Routing Scheme for flow classification and priority assignment[J].Journal of Engineering Research,2023,11(3):114-122.
[28]QI H,SI J,HOU J,et al.Subflow scheduling strategy for multipath transmission in SDN-based spatial network[J].Wireless Networks,2023,29(2):941-953.
[29]QI H,GUO Y,HOU D,et al.SDN-based dynamic multi-pathrouting strategy for satellite networks[J].Future Generation Computer Systems,2022,133:254-265.
[30]DONG C,XU X,LIU A,et al.Load balancing routing algorithm based on extended link states in LEO constellation network[J].China Communications,2022,19(2):247-260.
[31]GAYATRI V,SENTHIL K M.Efficient Load Balancing with MANET Propagation of Least Common Multiple Routing and Fuzzy Logic[J].Computers,Materials & Continua,2022,72(1):1831-1845.
[32]LUAN F,YANG J,ZHANG H,et al.Optimization of load-balancing strategy by self-powered sensor and digital twins in software-defined networks[J].IEEE Sensors Journal,2022,23(18):20782-20793.
[33]LATIF Z,LEE C,SHARIF K,et al.An SDN-based framework for load balancing and flight control in UAV networks[J].IEEE Consumer Electronics Magazine,2022,12(1):43-51.
[34]ZHAI D,LI H,TANG X,et al.Joint position optimization,user association,and resource allocation for load balancing in UAV-assisted wireless networks[J].Digital Communications and Networks,2022,10(1):25-37.
[35]TAO C C,ZHOU R.A method of UAV motion control to optimize air-ground relay network[J].Systems Engineering & Electronics,2024,46(5):1712-1723.
[36]CHEN X,MA R.Intelligent UAV planning for task-offloading with limited buffer and multiple computing servers[J].Physical Communication,2024,62:102240.
[37]ZHANG M,MOHAMMED E H,NG S X.Intelligent caching in UAV-aided networks[J].IEEE Transactions on Vehicular Technology,2021,71(1):739-752.
[38]MIAO Y,HWANG K,WU D,et al.Drone swarm path planning for mobile edge computing in industrial internet of things[J].IEEE Transactions on Industrial Informatics,2022,19(5):6836-6848.
[39]WANG D,TIAN J,ZHANG H,et al.Task offloading and trajectory scheduling for UAV-enabled MEC networks:An optimal transport theory perspective[J].IEEE Wireless Communications Letters,2021,11(1):150-154.
[40]ELGENDY I A,MESHOUL S,HAMMAD M.Joint task offloading,resource allocation,and load-balancing optimization in multi-UAV-aided MEC systems[J].Applied Sciences,2023,13(4):2625.
[41]ZHU Y,WANG S.Joint Deployment and Trajectory Planning of Multiple UAVs for Emergency Communications[C]//2023 IEEE Global Communications Conference.IEEE,2023:1854-1859.
[42]Multi-UAV Relay Connectivity Optimization for Heterogeneous Users Based on Load Balancing and Throughput Maximization [EB/OL].https://ieeexplore.ieee.org/abstract/document/10103874.
[43]GUO H,ZHOU X,WANG Y,et al.Achieve load balancing in multi-UAV edge computing IoT networks:A dynamic entry and exit mechanism[J].IEEE Internet of Things Journal,2022,9(19):18725-18736.
[44]SHAO S J,SU L L,GUO S Y,et al.Multi-Agent Cooperative Game Based Task Computing Mechanism for UAV-Assisted 6G NTN[J].Mobile Networks and Applications,2023,28(4):1510-1518.
[45]SAIF A,DIMYATI K,NOORDIN K A,et al.Skyward bound:Empowering disaster resilience with multi-UAV-assisted B5G networks for enhanced connectivity and energy efficiency[J].Internet of Things,2023,23:100885.
[46]SONBOL K,ÖZKASAP Ö,ALOQILY I,et al.EdgeKV:Decentralized,scalable,and consistent storage for the edge[J].Journal of Parallel and Distributed Computing,2020,144:28-40.
[47]QIAO F,WU J,LI J,et al.Trustworthy edge storage orchestration in intelligent transportation systems using reinforcement learning[J].IEEE Transactions on Intelligent Transportation Systems,2020,22(7):4443-4456.
[48]KONTODIMAS K,SOUMPLIS P,KRETSIS A,et al.Securedistributed storage on cloud-edge infrastructures[C]//2021 IEEE 10th International Conference on Cloud Networking(CloudNet).IEEE,2021:127-132.
[49]LI S,LAN T.HotDedup:Managing hot data storage at network edge through optimal distributed deduplication[C]//IEEE Conference on Computer Communications.IEEE,2020:247-256.
[50]YANG Y,YE M,JIANG Q,et al.A novel node selection method for wireless distributed edge storage based on SDN and a maldistributed decision model[J].Electronic Research Archive,2024,32(2):1160-1190.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
地址：重庆市渝北区洪湖西路18号    邮编：401121  
电话：023-63500828（编辑部） 023-67039612（会议/专题） 023-67039623（财务/发票）
E-mail：jsjkx12@163.com
本系统由北京玛格泰克科技发展有限公司设计开发