面向战场条件的无人机集群分布式存储方法

秦潜聪; 吴冠霖; 高原; 王双双; 李朋

doi:10.3969/j.issn.0258-2724.20230521

面向战场条件的无人机集群分布式存储方法

doi: 10.3969/j.issn.0258-2724.20230521

秦潜聪^{1, 2,},
吴冠霖^1, ,,
高原¹,
王双双¹,
李朋¹

1.
军事科学院战争研究院，北京 100091
2.
军事科学院研究生院，北京 100091

基金项目: 国家自然科学基金（62222121，62341110）

详细信息

作者简介:
秦潜聪（1997—），男，博士研究生，研究方向为指挥控制和体系工程，E-mail：570209535@qq.com

通讯作者:
吴冠霖（1993—），男，助理研究员，博士，研究方向为无人机、云计算，E-mail：wuguanlin16@nudt.edu.cn

中图分类号: V279；E91；TP333
计量
- 文章访问数: 397
- HTML全文浏览量: 112
- PDF下载量: 89
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-08
- 修回日期: 2024-01-08
- 网络出版日期: 2024-05-24
- 刊出日期: 2024-01-18

Distributed Storage Methods for Unmanned Aerial Vehicle Clusters in Battlefield

1.
Institute of War Studies, Academy of Military Science, Beijing 100091, China
2.
School of Graduate, Academy of Military Science, Beijing 100091, China

摘要

摘要:
无人机是世界军事强国执行战术行动的重要武器装备，利用无人机实现战场数据存储是保障作战的重要手段. 为更好地满足无人机集群在恶劣战场条件下数据存储需求，在分析无人机数据存储军事应用背景基础上，总结战场无人机数据资源特征和数据存储技术军事需求，并提出适用于我军无人机战术行动的数据存储设计方法流程；结合学术和产业界的成果，梳理数据存储技术的分类和发展历程，并总结数据存储的主要类型；提出面向无人集群战术行动场景的分布式数据存储关键技术方法和存储系统；介绍国内外学者在分布式存储海量数据处理、数据实时传输和数据可靠性等相关算法模型的研究现状；展望未来战术行动中无人机数据运用方法的研究趋势，强调在战场条件下无人机集群数据存储是未来重要的研究领域.
- 无人机 /
- 无人机集群 /
- 战场条件 /
- 战术行动 /
- 数据存储 /
- 分布式存储
Abstract:
Unmanned aerial vehicles (UAVs) are important weapons and equipment for military powers in the world to carry out tactical operations. Applying UAVs in the battlefield to achieve data storage is an important way to ensure the operation. In order to better meet the data storage requirements of UAV clusters under harsh battlefield conditions, the military application background of UAV data storage was analyzed, and the data resources characteristics of UAVs in the battlefield and military requirements of data storage technologies were summarized. The design process of data storage method suitable for tactical operations of UAVs from Chinese armed forces was put forward. Then, on the basis of the achievements of data storage in the field of academia and industry, the classification and development process of data storage technologies were reviewed, and the main types of data storage were summarized. The key technologies and storage systems of distributed data storage for tactical operations of UAV clusters were presented. Moreover, the research status of the algorithm models of distributed mass data storage and processing, real-time data transmission, and data reliability in China and abroad was introduced. The research direction for the data application method of UAVs in tactical operations was proposed, and it was pointed out that data storage of UAV clusters in battlefields is an important research field in the future.
- UAV /
- UAV cluster /
- battlefield condition /
- tactical operations /
- data storage /
- distributed storage

HTML全文

图 1 无人机战术行动数据存储设计方法流程

Figure 1. Design process of data storage method for tactical operations of UAVs

下载: 全尺寸图片幻灯片

图 2 3种数据存储方式

Figure 2. Three data storage methods

下载: 全尺寸图片幻灯片

图 3 集中式存储模型

Figure 3. Centralized storage model

下载: 全尺寸图片幻灯片

图 4 分布式存储模型

Figure 4. Distributed storage model

下载: 全尺寸图片幻灯片

图 5 云存储服务模式

Figure 5. Cloud storage service pattern

下载: 全尺寸图片幻灯片

表 1 美军无人机数据链

Table 1. UAV data link of US army

无人机类型	数据链路类型	工作波段	数据传输率/（Mbit·s⁻¹）	存储量/GB
捕食者无人机	通用数据链（CDL）	X、Ku	10.71～274.00	≥1024
猎人、E-8、先锋无人机	战术通用数据链（TCDL）	Ku	10.71～200.00	≥1024
战术无人机	战术数字数据链（TDDL）	S、C、X、Ku	10.71～274.00	≥2 048
火力侦察无人机	高完整性数据链路（HIDL）	UHF	4.00～20.00	≥1024

下载: 导出CSV

表 2 分布式存储系统总结

Table 2. Summary of distributed storage systems

类型	来源	适用场景	优势	劣势
GFS	文献[39-41]	大规模数据存储	容错机制、可部署于廉价机器、自动负载均衡	特定于大数据场景、单点失效问题
Ceph	文献[42-47]	统一存储、云储存	开源系统，功能强大、高可扩展性、高可用性	底层结构数据为对象，性能上限不高；运维成本高
GlusterFS	文献[48-51]	大文件存储	开源系统、节点对等结构、具备强大横向扩展能力、支持运行任何标准IP网络	遍历目录下文件耗时、海量小文件存储能力弱、配置信息变化需要时间同步
Amaz S3	文献[52-57]	对象存储	采用对象存储、提供了统一的接口 REST/SOAP 来统一访问任何数据	亚马逊公司非开源产品，战场特殊数据存储场景无法完全适用
MooseFS	文献[58-61]	中小规模轻量型应用	高可扩展性、灵活性高和易于部署配置、适用于中小规模轻量型应用、支持多机冗余备份	受主服务器的性能限制、主服务器内存的需求量、元数据复制时间较长
OneFS	文献[62-63]	大数据存储、非结构化数据存储	支持IP地址文件控制、灵活简单、支持多线程不同文件并发写入	外国戴尔公司收费服务产品，战场特殊数据存储场景无法完全适用
IPFS	文献[64-67]	文件存储	数据架构简单；避免文件内容相同重复存储；类似区块链的不可变数据存储，持续性更强	保持文件可用性易受到节点影响，用户无法主动删除文件

下载: 导出CSV

表 3 分布式存储海量数据研究文献总结

Table 3. Review of research on distributed storage of massive data

技术手段	来源	研究对象	主要方法
负载均衡	文献[68]	地理分布式存储系统	哈希算法
	文献[69]	存储系统	哈希算法
	文献[70]	分布式文件系统	随机算法
	文献[71]	城市视频存储系统	动态负载均衡算法
	文献[72]	分布式文件系统	哈希算法
	文献[73]	计算机存储器	ISP 架构
	文献[74]	企业云平台存储服务	SkyMax 算法
	文献[75]	报文分类存储	SDLBA 算法
	文献[76]	云计算	蜂群负载算法
	文献[77-79]	算法模型	循环、蚁群、禁忌搜索算法
数据压缩	文献[80]	分布式文件系统	动态选择算法
	文献[81]	文本数据	CBC 算法
	文献[82]	表格数据	语义压缩算法
	文献[83]	图像数据	高效图形压缩算法
	文献[84]	不规则序列	数据压缩算法
	文献[85-87]	算法模型	时间序列、检测、多分辨率压缩算法

下载: 导出CSV

表 4 分布式存储实时数据研究文献总结

Table 4. Review of research on distributed storage of real-time data

技术手段	文献	研究对象	主要方法
数据流处理	[88]	分布式数据流	GALOIS 边缘数据流处理架构
	[89]	边缘计算数据流	优化处理框架
	[90]	分布式数据流	数据分配算法
	[91]	数据流异常值检测	局部离群因子算法
	[92]	异构资源数据流	自主分层伸缩策略
	[93]	动态数据流聚类	在线聚类算法
	[94]	数据流动态变化	OVRMAXGB 算法
实时数据库	[96]	实时数据推送	基于集合的实时推送方法
	[97]	网络视频监控数据存储	本地数据库缓存模型
	[98]	电网数据实时存储	分布式NoSQL 数据库
	[99]	船舶控制系统实时数据	分布式实时数据库同步技术方案

下载: 导出CSV

表 5 分布式存储数据抗毁研究文献总结

Table 5. Review of research on distributed storage data survivability

技术手段	来源	研究对象	主要方法
数据备份	文献[101-102]	地理分布式数据中心	ILP 模型数据备份方案
	文献[103]	云平台数据	类型区分的数据复制技术
	文献[104]	分布式备份存储系统	数据放置算法
	文献[105]	磁盘备份和恢复	数据合并算法
	文献[106]	云平台数据	备份服务器数据访问框架
	文献[107]	云平台数据	三角洲压缩算法
数据容灾	文献[108]	分布式数据存储容灾	Slogger 数据灾备架构
	文献[109]	网络突变数据容灾	DELTA 压缩算法
	文献[110]	渐进式网络恢复	启发式算法
	文献[111]	云平台数据	区块链分布式多副本数据存储
	文献[112]	医疗数据	远程复制技术

下载: 导出CSV

参考文献(112)

[1]	刘书雷,徐海洋. 无人机在军事行动中的作用评析[J]. 国际社会科学杂志(中文版),2023,40(1): 183-186,7,12. LIU Shulei, XU Haiyang. An analysis of the function of drones in military actions[J]. International Social Science Journal (Chinese Edition), 2023, 40(1): 183-186,7,12.
[2]	王立磊,魏启航. 对我军无人机力量建设的几点思考[J]. 兵工自动化,2020,39(10): 1-5. WANG Lilei, WEI Qihang. Reflections on construction of UAV forces of our army[J]. Ordnance Industry Automation, 2020, 39(10): 1-5.
[3]	CHATURVEDI S K, SEKHAR R, BANERJEE S, et al. Comparative review study of military and civilian unmanned aerial vehicles (UAVs)[J]. INCAS Bulletin, 2019, 11(3): 183-198. doi: 10.13111/2066-8201.2019.11.3.16
[4]	吴大辉. 乌克兰危机与新军事革命:无人机篇[J]. 世界知识,2023(11): 72-73.
[5]	WISWESSER S M. Potemkin on the Dnieper: the failure of Russian Airpower in the Ukraine war[J]. Small Wars & Insurgencies, 2023, 34: 1205-1234.
[6]	MOHAMMAD E. Iran’s drone supply to Russia and changing dynamics of the Ukraine war[J]. Journal for Peace and Nuclear Disarmament, 2022, 5(2): 507-518. doi: 10.1080/25751654.2022.2149077
[7]	RADUNTSEV M V, SEREBRYAKOV A S, TIKHONOV A I. Analysis of the Russian regulatory framework for drone development, certification, and use[J]. Russian Engineering Research, 2022, 42(1): S109-S113.
[8]	NALLAMALLI R, SINGH K, KUMAR I D. Technological perspectives of countering UAV swarms[J]. Defence Science Journal, 2023, 73(4): 420-428. doi: 10.14429/dsj.73.18695
[9]	LIU W, HE Y. Application of US military data link in typical weapon and equipment[J]. Journal of Engineering Mechanics and Machinery, 2022, 7: 070301.1-070301.5.
[10]	王毓龙,周阳升,李从云. 美军无人机数据链发展研究[J]. 飞航导弹,2013(4): 73-76.
[11]	雷鹏勇,刘胜春,贺岷珏,等. 电子战数据链的需求分析与发展趋势[J]. 电子信息对抗技术,2020,35(2): 44-47. doi: 10.3969/j.issn.1674-2230.2020.02.011 LEI Pengyong, LIU Shengchun, HE Minjue, et al. Analysis of requirement and development on data link for electronic warfare[J]. Electronic Information Warfare Technology, 2020, 35(2): 44-47. doi: 10.3969/j.issn.1674-2230.2020.02.011
[12]	许银龙,都安平. 战场实时综合态势信息分发技术研究[J]. 中国电子科学研究院学报,2022,17(2): 193-198,206. doi: 10.3969/j.issn.1673-5692.2022.02.015 XU Yinlong, DU Anping. Resesrch on distributing technology of real-time comprehensive situatuinon information in battlefield[J]. Journal of China Academy of Electronics and Information Technology, 2022, 17(2): 193-198,206. doi: 10.3969/j.issn.1673-5692.2022.02.015
[13]	唐博建. 实时战场态势驱动的智能决策支持技术[D]. 南京: 南京大学,2021.
[14]	吴云章,肖阳,王丽萍,等. 基于实时数据驱动的低空数字平行战场研究[J]. 国防科技,2022,43(6): 114-122,134. WU Yunzhang, XIAO Yang, WANG Liping, et al. Research on real-time data-driven low-altitude digital parallel battlefields[J]. National Defense Technology, 2022, 43(6): 114-122,134.
[15]	ZHU X N. Analysis of military application of UAV swarm technology[C]//The 3rd International Conference on Unmanned Systems (ICUS). Harbin: IEEE, 2020: 1200-1204.
[16]	GREENGARD S. The future of data storage[J]. Communications of the ACM, 2019, 62(4): 3311723.1-3311723.12.
[17]	HORIMAI H, TAN X D. Holographic information storage system: today and future[J]. IEEE Transactions on Magnetics, 2007, 43(2): 943-947. doi: 10.1109/TMAG.2006.888528
[18]	WU J Y, PING L D, GE X P, et al. Cloud storage as the infrastructure of cloud computing[C]//International Conference on Intelligent Computing and Cognitive Informatics. Kuala Lumpur: IEEE, 2010: 380-383.
[19]	JAIKAR A, SHAH S A R, NOH S Y, et al. Performance analysis of NAS and SAN storage for scientific workflow[C]//International Conference on Platform Technology and Service (PlatCon). Jeju: IEEE, 2016: 1-4.
[20]	SACKS D. Demystifying storage networking DAS, SAN, NAS, NAS gateways, fibre channel, and iSCSI[EB/OL]. [2023-08-14]. https://people.eecs.berkeley.edu/~randy/Courses/CS294.S13/12.1.pdf.
[21]	RAVI KUMAR M G. Network-attached storage: data storage applications[J]. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(12): 2385-2396.
[22]	KAI Z. Research on network data storage technology based on autonomous controllable system[C]//2021 International Conference on Computer Engineering and Artificial Intelligence (ICCEAI). Shanghai: IEEE, 2021: 183-186.
[23]	CHOI J H. File recovery method in NTFS-based damaged RAID system[J]. Human-Centric Computing and Information Sciences, 2022, 12: 40.1-40.13.
[24]	CHUKRY S, SBEYTI H. Security enhancement in storage area network[C]//The 7th International Symposium on Digital Forensics and Security (ISDFS). Barcelos: IEEE, 2019: 1-5.
[25]	ZHANG H K, YAN Z, LIANG X Q. A survey on data security in network storage systems[C]//IEEE International Conferences on Internet of Things (iThings) and IEEE Green Computing & Communications (GreenCom) and IEEE Cyber, Physical & Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics). Espoo: IEEE, 2022: 320-327.
[26]	BURGER S P, JENKINS J D, HUNTINGTON S C, et al. Why distributed? a critical review of the tradeoffs between centralized and decentralized resources[J]. IEEE Power and Energy Magazine, 2019, 17(2): 16-24. doi: 10.1109/MPE.2018.2885203
[27]	JAISWAL S, RAJ S, SIDHANTA S, et al. A lightweight, mobility-aware, geospatial & temporal data store for multi-UAV systems[C]//IEEE/ACM the 23rd International Symposium on Cluster, Cloud and Internet Computing Workshops (CCGridW). Bangalore: IEEE, 2023: 305-307.
[28]	LIM J, YE J, KIM J, et al. Neural cloud storage: innovative cloud storage solution for cold video[C]//Proceedings of the 15th ACM Workshop on Hot Topics in Storage and File Systems. Boston: ACM, 2023: 1-7.
[29]	SHARMA P, JINDAL R, BORAH M D. Blockchain technology for cloud storage: a systematic literature review[J]. ACM Computing Surveys, 53(4): 89.1-89.32.
[30]	ZENG W Y, ZHAO Y L, OU K R, et al. Research on cloud storage architecture and key technologies[C]//Proceedings of the 2nd International Conference on Interaction Sciences: Information Technology, Culture and Human. Seoul: ACM, 2009: 1044-1048.
[31]	GUAN. S P, ZHANG C H, WANG Y L, et al. Hadoop-based secure storage solution for big data in cloud computing environment[J]. Digital Communications and Networks,2024,10(1):227-236.
[32]	HE Q L, LI Z H, ZHANG X. Study on cloud storage system based on distributed storage systems[C]// International Conference on Computational and Information Sciences. Chengdu: IEEE, 2010: 1332-1335.
[33]	MERCL L, PAVLIK J. Public cloud Kubernetes storage performance analysis[C]//International Conference on Computational Collective Intelligence. Cham: Springer, 2019: 649-660.
[34]	KHATAMI A A, PURWANTO Y, RURIAWAN M F. High availability storage server with Kubernetes[C]// International Conference on Information Technology Systems and Innovation (ICITSI). Bandung: IEEE, 2020: 74-78.
[35]	CHEN H W, YU J P, LIU F, et al. Archipelago: a medical distributed storage system for interconnected health[J]. IEEE Internet Computing, 2020, 24(2): 28-38. doi: 10.1109/MIC.2019.2963182
[36]	VARGAS J C G, TOAPANTA S T, QUINTANA Y J M, et al. Blockchain model based on human DNA to modify blocks in P2P networks[C]//International Conference on Computer, Information and Telecommunication Systems (CITS). Genoa: IEEE, 2023: 1-7.
[37]	KAUR H, JAMEEL R, ALAM M A, et al. Securing and managing healthcare data generated by intelligent blockchain systems on cloud networks through DNA cryptography[J]. Journal of Enterprise Information Management, 2023, 36(4): 861-878. doi: 10.1108/JEIM-02-2021-0084
[38]	BUKO T, TUCZKO N, ISHIKAWA T. DNA data storage[J]. BioTech, 2023, 12(2): 44-60. doi: 10.3390/biotech12020044
[39]	PANDEY R, SAH S P. A review on google file system[J]. International Journal of Computer Science Trends and Technology (IJCST), 2016, 4: 177-180.
[40]	WANG M D, LI B, ZHAO Y X, et al. Formalizing google file system[C]//The 20th Pacific Rim International Symposium on Dependable Computing. Singapore: IEEE, 2014: 190-191.
[41]	YANG J. From google file system to omega: a decade of advancement in big data management at google[C]//The First International Conference on Big Data Computing Service and Applications. Redwood City: IEEE, 2015: 249-255.
[42]	WEIL S A, BRANDT S A, MILLER E L, et al. Ceph: a scalable, high-performance distributed file system[C]//Proceedings of the 7th symposium on Operating systems design and implementation. Seattle: ACM, 2006: 307-320.
[43]	WANG F Y, NELSON M, ORAL S, et al. Performance and scalability evaluation of the Ceph parallel file system[C]//Proceedings of the 8th Parallel Data Storage Workshop. Denver: ACM, 2013: 14-19.
[44]	张晓,张思蒙,石佳,等. Ceph分布式存储系统性能优化技术研究综述[J]. 计算机科学,2021,48(2): 1-12. doi: 10.11896/jsjkx.201000149 ZHANG Xiao, ZHANG Simeng, SHI Jia, et al. Review on performance optimization of Ceph distributed storage system[J]. Computer Science, 2021, 48(2): 1-12. doi: 10.11896/jsjkx.201000149
[45]	陶锐哲,谢涛涛,尹萍,等. iSCSI转发Ceph存储的性能影响因素评价[J]. 信息技术与信息化,2023(8): 79-83,90. doi: 10.3969/j.issn.1672-9528.2023.08.017
[46]	夏亚楠,王勇. Ceph存储系统中节点的容错选择算法[J]. 桂林电子科技大学学报,2022,42(5): 384-390. XIA Yanan, WANG Yong. A fault tolerant nodes selection algorithm in Ceph storage system[J]. Journal of Guilin University of Electronic Technology, 2022, 42(5): 384-390.
[47]	黄遵祥,朱磊基,熊勇. 一种基于双控节点的Ceph写性能优化方法[J]. 中国科学院大学学报,2022,39(6): 817-826. HUANG Zunxiang, ZHU Leiji, XIONG Yong. A Ceph write performance optimization method based on double-control nodes[J]. Journal of University of Chinese Academy of Sciences, 2022, 39(6): 817-826.
[48]	胡华俊. 分布式文件系统GlusterFS架构以及性能优化的研究[D]. 成都: 电子科技大学,2017.
[49]	李鹤鸣. 云平台下基于GlusterFS的多客户端文件管理系统[D]. 成都: 电子科技大学,2021.
[50]	李立. 基于GlusterFS的分级云存储系统设计与实现[D]. 长沙: 国防科技大学,2017.
[51]	张富成,付绍静,夏竟,等. 基于GlusterFS的分布式数据完整性验证系统[J]. 信息网络安全,2021,21(1): 72-79. doi: 10.3969/j.issn.1671-1122.2021.01.009 ZHANG Fucheng, FU Shaojing, XIA Jing, et al. GlusterFS-based distributed data integrity verification system[J]. Netinfo Security, 2021, 21(1): 72-79. doi: 10.3969/j.issn.1671-1122.2021.01.009
[52]	张楠. 亚马逊云科技重塑云中数据活力[J]. 软件和集成电路,2022(9): 66-68. doi: 10.3969/j.issn.2096-062X.2022.9.rjhjcdl202209027
[53]	张聚胜. 驾车场景到达时间预估系统设计与实现[D]. 北京: 北京交通大学,2022.
[54]	赵洋艺. 某游戏发行公司移动数据统计分析系统的设计与实现[D]. 西安: 西安电子科技大学,2020.
[55]	方琍. 基于AWS无服务器架构的房地产行业大数据分析与应用方案[J]. 信息与电脑(理论版),2020,32(10): 157-159. FANG Li. Big data analytics applications for the real estate industry based on AWS serverless architecture[J]. China Computer & Communication, 2020, 32(10): 157-159.
[56]	翟大海,刘苗,李金珂,等. 基于对象存储的代理式加密系统设计与实现[J]. 现代传输,2022(3): 69-74. doi: 10.3969/j.issn.1673-5137.2022.03.009
[57]	李敏达. 基于Amazon S3 API的分布式对象存储系统设计与实现[D]. 武汉: 华中科技大学,2021.
[58]	姚孝珍. 云平台下基于MooseFS的网盘系统架构及关键技术的设计与实现[D]. 成都: 电子科技大学,2020.
[59]	陈娟. 面向分布式文件系统的数字取证研究[D]. 南京: 东南大学,2019.
[60]	娄祯骥. 面向异构执行体池的动态防御机制的研究[D]. 北京: 北京邮电大学,2022.
[61]	林炳东,赵旦谱,台宪青. MooseFS热备元数据节点设计与实现[J]. 计算机工程与应用,2019,55(13): 72-77. doi: 10.3778/j.issn.1002-8331.1802-0006 LIN Bingdong, ZHAO Danpu, TAI Xianqing. Design and implementation of MooseFS hot standby master[J]. Computer Engineering and Applications, 2019, 55(13): 72-77. doi: 10.3778/j.issn.1002-8331.1802-0006
[62]	LOHR A, THEBARGE J, KWONG M, et al. Victr: virtualized infrastructure with cyber and testbed resources[C]//Proceedings of the 14th annual International Conference of Education, Research and Innovation. Valencia: IATED, 2021: 9628-9635.
[63]	MARCILLO F, DIAZ A F, PALACIOS R H, et al. Evaluating erasure codes in Dicoogle PACS[J]. IEEE Access, 2022,10: 71874-71885.
[64]	董天一,戴嘉乐,黄禹铭. IPFS原理与实践[M]. 北京: 机械工业出版社, 2019.
[65]	DANIEL E, TSCHORSCH F. IPFS and friends: a qualitative comparison of next generation peer-to-peer data networks[J]. IEEE Communications Surveys & Tutorials, 2022, 24(1): 31-52.
[66]	DOAN T V, PSARAS Y, OTT J, et al. Towards decentralised cloud storage with IPFS: opportunities, challenges, and future directions[EB/OL]. (2022-03-13)[2023-08-17]. http://arxiv.org/abs/2202.06315.
[67]	KUMAR S, BHARTI A K, AMIN R. Decentralized secure storage of medical records using Blockchain and IPFS: a comparative analysis with future directions[J]. Security and Privacy, 2021, 4(5): e162.1-e162.16.
[68]	BOGDANOV K L, REDA W, MAGUIRE G Q, et al. Fast and accurate load balancing for geo-distributed storage systems[C]//Proceedings of the ACM Symposium on Cloud Computing. Carlsbad: ACM, 2018: 386-400.
[69]	LÖSCH R, SCHMIDT J, FELDE N G. Weighted load balancing in distributed hash tables[C]//Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services. Munich: ACM, 2019: 473-482.
[70]	LIU L, FORTNOW L, LI J, et al. Randomized algorithms for dynamic storage load-balancing[C]//Proceedings of the Seventh ACM Symposium on Cloud Computing. Santa Clara: ACM, 2016: 210-222.
[71]	LI Y. Dynamic load balancing method for urban surveillance video big data storage based on HDFS[C]//Proceedings of the 7th International Conference on Computing and Artificial Intelligence. Tianjin: ACM, 2021: 160-167.
[72]	AGHAYEV A, WEIL S, KUCHNIK M, et al. The case for custom storage backends in distributed storage systems[J]. ACM Transactions on Storage, 16(2): 9.1-9.31.
[73]	HEYDARIGORJI A, REZAEI S, TORABZADEHKASHI M, et al. Leveraging computational storage for power-efficient distributed data analytics[J]. ACM Transactions on Embedded Computing Systems, 21(6): 82.1-82.36.
[74]	KAUSHIK Y, BHOLA A, JHA C K. Proposed SKYMAX load balancing algorithm[C]//Proceedings of the International Conference on Advances in Information Communication Technology & Computing. Bikaner: ACM, 2016: 1-5.
[75]	MAN D P, YANG W, TIAN G Q. Polymorphic load balancing algorithm based on packet classification[C]//Proceedings of the 2nd International Conference on Telecommunications and Communication Engineering. Beijing: ACM, 2018: 258-261.
[76]	PENG H Y, HAN W G, YAO J, et al. The realization of load balancing algorithm in cloud computing [C]//Proceedings of the 2nd International Conference on Computer Science and Application Engineering. Hohhot: ACM, 2018: 1-5.
[77]	KATYAL M, MISHRA A. A comparative study of load balancing algorithms in cloud computing environment[J]. International Journal of Distributed and Cloud Computing, 2013, 1(2): 5-14.
[78]	GABHANE J P, PATHAK S, THAKARE N M. A novel hybrid multi-resource load balancing approach using ant colony optimization with Tabu search for cloud computing[J]. Innovations in Systems and Software Engineering, 2023, 19(1): 81-90. doi: 10.1007/s11334-022-00508-9
[79]	HOTA A, MOHAPATRA S, MOHANTY S. Survey of different load balancing approach-based algorithms in cloud computing: a comprehensive review[C]// Computational Intelligence in Data Mining. Singapore: Springer, 2019: 99-110.
[80]	WANG F Z, GUO H L, ZHAO J. Dynamic data compression algorithm selection for big data processing on local file system[C]//Proceedings of the 2nd International Conference on Computer Science and Artificial Intelligence. Shenzhen: ACM, 2018: 110-114.
[81]	GAO F, DUTTA A, LIU J J. Content-based textual big data analysis and compression[C]//Proceedings of the International Conference on Computing and Big Data. Charleston: ACM, 2018: 7-12.
[82]	ILKHECHI A, CROTTY A, GALAKATOS A, et al. DeepSqueeze: deep semantic compression for tabular data[C]//Proceedings of the International Conference on Management of Data. Portland: ACM, 2020: 1733-1746.
[83]	CHEN Z, ZHANG F, GUAN J W, et al. CompressGraph: efficient parallel graph analytics with rule-based compression[J]. Proceedings of the ACM on Management of Data, 2023, 1(1): 4.1-4.31.
[84]	ZHU R. Compression algorithm based on irregular sequence[C]//Proceedings of the 4th International Conference on Graphics and Signal Processing. Nagoya: ACM, 2020: 103-106.
[85]	CHIAROT G, SILVESTRI C. Time series compression survey[J]. ACM Computing Surveys, 2021, 55(10): 198.1-198.32.
[86]	YU P F, HUANG X L, HU B, et al. A JPEG compression-resistant data watermark embedding and detection algorithm[C]//Proceedings of the 5th International Conference on Electronic Information Technology and Computer Engineering. Xiamen: ACM, 2021: 1219-1226.
[87]	BARBARIOLI B, MERSY G, SINTOS S, et al. Hierarchical residual encoding for multiresolution time series compression[J]. Proceedings of the ACM on Management of Data, 2023, 1(1): 99.1-99.26.
[88]	STOLZ T, KOREN I, TIRPITZ L, et al. GALOIS: a hybrid and platform-agnostic stream processing architecture[C]//Proceedings of the International Workshop on Big Data in Emergent Distributed Environments. Seattle: ACM, 2023: 1-6.
[89]	AMARASINGHE G, DE ASSUNÇÃO M D, HARWOOD A, et al. A data stream processing optimisation framework for edge computing applications[C]//The 21st International Symposium on Real-Time Distributed Computing (ISORC). Singapore: IEEE, 2018: 91-98.
[90]	CHEN Y Q, ZHENG L J, LIU W N. Performance-sensitive data distribution method for distributed stream processing systems[C]//Proceedings of the 4th High Performance Computing and Cluster Technologies Conference & the 3rd International Conference on Big Data and Artificial Intelligence. Qingdao: ACM, 2020: 212-217.
[91]	ALGHUSHAIRY O, ALSINI R, MA X G, et al. A genetic-based incremental local outlier factor algorithm for efficient data stream processing[C]// Proceedings of the 4th International Conference on Compute and Data Analysis. Silicon Valley: ACM, 2020: 38-49.
[92]	RUSSO G R, CARDELLINI V, PRESTI F L. Hierarchical auto-scaling policies for data stream processing on heterogeneous resources[J]. ACM Transactions on Autonomous and Adaptive Systems, 2023, 18(4): 14.1-14.44.
[93]	GAO D Z. An autoencoder-based fast online clustering algorithm for evolving data stream[C]//Proceedings of the 2nd Asia Conference on Algorithms, Computing and Machine Learning. Shanghai: ACM, 2023: 90-95.
[94]	LUO X C, LI D W, ZHANG H Q, et al. Multi-classification data stream algorithm based on one-vs-rest strategy[C]//Proceedings of the 3rd International Conference on Artificial Intelligence, Automation and Algorithms. Beijing: ACM, 2023: 66-72.
[95]	CARDELLINI V, LO PRESTI F, NARDELLI M, et al. Runtime adaptation of data stream processing systems: the state of the art[J]. ACM Computing Surveys, 2022, 54(11): 1-36.
[96]	WINGERATH W, GESSERT F, RITTER N. InvaliDB: scalable push-based real-time queries on top of pull-based databases[C]//The 36th International Conference on Data Engineering (ICDE). Dallas: IEEE, 2020: 1874-1877.
[97]	HIDAYAT M N, SETIAWAN R B, RONILAYA F. Real time sensor monitoring using local database cache method[C]//International Conference on Electrical and Information Technology (IEIT). Malang: IEEE, 2022: 115-119.
[98]	LIU P, DENG C Y, WANG D Z. Design and application of distributed real-time database system for massive data[C]//The 3rd International Conference on Computer Science and Management Technology (ICCSMT). Shanghai: IEEE, 2022: 114-117.
[99]	JIE S, JIAN Z. Research on synchronization technology of peer-to-peer distributed real-time database based on ship platform[C]//International Symposium on Computer Engineering and Intelligent Communications (ISCEIC). Guangzhou: IEEE, 2020: 70-75.
[100]	ZHAO Y M, LU N. Research and implementation of data storage backup[C]//International Conference on Energy Internet (ICEI). Beijing: IEEE, 2018: 181-184.
[101]	MA L S, YANG B. Data backup against progressive disasters in geo-distributed data center networks[C]//International Conference on Networking and Network Applications (NaNA). Xi’an: IEEE, 2018: 223-226.
[102]	MA L S, YANG B. Data backup in geo-distributed data center networks under time and budget constraints[C]//International Conference on Networking and Network Applications (NaNA). Daegu: IEEE, 2019: 155-159.
[103]	VIJAYALAKSHMI K, JAYALAKSHMI V. Analysis on data deduplication techniques of storage of big data in cloud[C]//The 5th International Conference on Computing Methodologies and Communication (ICCMC). Erode: IEEE, 2021: 976-983.
[104]	RENUGA K, TAN S S, ZHU Y Q, et al. Balanced and efficient data placement and replication strategy for distributed backup storage systems[C]//International Conference on Computational Science and Engineering. Vancouver: IEEE, 2009: 87-94.
[105]	WU G J, YUN X C, WANG S P. Design and implementation of multi-version disk backup data merging algorithm[C]//The Ninth International Conference on Web-Age Information Management. Zhangjiajie: IEEE, 2008: 526-531.
[106]	SUMAN O P, SAINI L K, KUMAR S. Cloud-based data protection and secure backup solutions: a comprehensive review of ensuring business continuity[C]//The Third International Conference on Secure Cyber Computing and Communication (ICSCCC). Jalandhar: IEEE, 2023: 821-826.
[107]	WANG Q. Cloud data backup and recovery method based on the DELTA compression algorithm[C]// International Conference on Industrial Application of Artificial Intelligence (IAAI). Harbin: IEEE, 2021: 183-188.
[108]	ALQURAAN A, KOGAN A, MARATHE V J, et al. Scalable, near-zero loss disaster recovery for distributed data stores[J]. Proceedings of the VLDB Endowment, 2020, 13(9): 1429-1442. doi: 10.14778/3397230.3397239
[109]	CHEN F. Disaster recovery method for network surge and sudden change data based on DELTA compression algorithm[C]//The 3rd International Conference on Artificial Intelligence and Advanced Manufacture. Manchester: ACM, 2021: 1926-1930.
[110]	ZHANG Q L, AYOUB O, WU J, et al. Progressive slice recovery with guaranteed slice connectivity after massive failures[J]. IEEE/ACM Transactions on Networking, 2022, 30(2): 826-839. doi: 10.1109/TNET.2021.3130576
[111]	LIU B, XIN Y, ZHANG C Y. A solution for A disaster recovery service system in multi-cloud environment [C]//International Applied Computational Electromagnetics Society Symposium (ACES-China). Xuzhou: IEEE, 2022: 1-4.
[112]	WANG Y J, GONG L, ZHANG M. Remote disaster recovery and backup of rehabilitation medical archives information system construction under the background of big data[C]//International Conference on Sustainable Computing and Data Communication Systems (ICSCDS). Erode: IEEE, 2022: 575-578.