建模与仿真服务化研究综述

张淼; 许凯; 彭勇; 尹全军

doi:10.16383/j.aas.c220555

建模与仿真服务化研究综述

doi: 10.16383/j.aas.c220555

张淼^1,,
许凯^1,,
彭勇^1,,
尹全军^1,

1.
国防科技大学系统工程学院长沙 410073

基金项目: 国家自然科学基金(62103420)资助

详细信息

作者简介:
张淼：国防科技大学系统工程学院讲师. 2022年获得国防科技大学博士学位. 主要研究方向为云仿真. E-mail: zhangmiao15@nudt.edu.cn

许凯：国防科技大学系统工程学院讲师. 2020年获得国防科技大学博士学位. 主要研究方向为行为建模, 云仿真. E-mail: xukai09@nudt.edu.cn

彭勇：国防科技大学系统工程学院副研究员. 2011年获得国防科技大学博士学位. 主要研究方向为并行与分布式仿真, 云仿真. 本文通信作者. E-mail: yongpeng@nudt.edu.cn

尹全军：国防科技大学系统工程学院研究员. 2005年获得国防科技大学博士学位. 主要研究方向为行为建模, 云仿真. E-mail: yin_quanjun@163.com

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出版历程
- 收稿日期: 2022-07-06
- 录用日期: 2022-12-10
- 网络出版日期: 2023-03-28
- 刊出日期: 2023-08-21

Overview of Servitization of Modeling and Simulation

ZHANG Miao^1
,,
XU Kai^1
,,
PENG Yong^1
,,
YIN Quan-Jun^1
,

1.
College of Systems Engineering, National University of Defense Technology, Changsha 410073

Funds: Supported by National Natural Science Foundation of China (62103420)

More Information

Author Bio:
ZHANG Miao　Lecturer at the College of Systems Engineering, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 2022. His main research interest is cloud simulation

XU Kai　Lecturer at the College of Systems Engineering, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 2020. His research interest covers behavior modeling and cloud simulation

PENG Yong　Associate researcher at the College of Systems Engineering, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 2011. His research interest covers parallel and distributed simulation and cloud simulation. Corresponding author of this paper

YIN Quan-Jun　Researcher at the College of Systems Engineering, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 2005. His research interest covers behavior modeling and cloud simulation

摘要

摘要: 建模与仿真服务化是提升用户体验, 支撑按需访问建模与仿真能力的有效手段. 本文首先从建模与仿真服务的访问、开发以及运行与管理三个层面对建模与仿真服务化的概念进行辨析; 并从服务的分类、抽象层级、基本元素和状态四个角度对建模与仿真服务的特征进行阐述. 然后从基于网页的仿真、基于面向服务架构 (Service oriented architecture, SOA) 的仿真系统开发和服务化基础设施三个维度对建模与仿真服务化的发展历程进行梳理. 在此基础之上, 分析了基于云的建模与仿真服务化的构建原则、基本架构和应用模式, 并从访问、开发以及运行与管理三个层面给出建模与仿真服务化相关的支撑技术. 最后, 从理论体系、关键技术和新兴技术三个方面给出进一步发展建模与仿真服务化的建议.
- 仿真服务 /
- 建模与仿真 /
- 云仿真 /
- 面向服务架构 /
- 建模仿真即服务
Abstract: Cloud-based modeling and simulation (M&S) servitization can effectively improve user experience and the efficiency in developing simulations. In this paper, we firstly illustrate the concept of M&S servitization from three aspects, namely application, development, operation and management of M&S services. The connotation of modeling and simulation service is explained from four aspects, namely the categories, abstraction levels, constituent elements and states of services. Then, the development and evolution of modeling and simulation servitization is summarized from three aspects, namely web-based simulation, SOA (service oriented architecture)-based simulation development as well as operation and management of simulation on service-oriented infrastructure. On this basis, the construction principle, basic architecture and application mode of cloud-based modeling and simulation servitization are analyzed, and the supporting technologies related to modeling and simulation servitization are given from the views of application, development and operation and management. Finally, suggestions for further development of M&S servitization are given from three aspects: Theoretical systems, key technologies and emerging technologies.
- Simulation service /
- modeling and simulation /
- cloud simulation /
- service oriented architecture (SOA) /
- modeling and simulation as a service (MSaaS)

HTML全文

图 1 MSaaS服务模型^[16]

Fig. 1 Service model in MSaaS^[16]

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图 2 建模与仿真服务化通用架构

Fig. 2 General architecture of MSaaS

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图 3 建模与仿真服务化应用示意图

Fig. 3 Application schematic diagram of MSaaS

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图 4 云仿真部署形态示意图^[16]

Fig. 4 Schematic diagram of cloud simulation deployment form^[16]

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图 5 建模与仿真服务化关键支撑技术

Fig. 5 Key supporting technologies for MSaaS

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表 1 与现有相关综述的异同

Table 1 Differences and similarities with existing reviews

文献名称	发表时间	主要内容	与本文异同点
Modeling and simulation as a cloud service: A survey^[16]	2013	首次给出MSaaS的服务模型, 探讨MSaaS架构和部署模式, 分析MSaaS可能面临的安全威胁, 简要介绍服务组合技术	1)本文同样对MSaaS的服务模型、架构、部署模式进行探讨; 2)该文缺乏对MSaaS发展历程的梳理, 且对实现MSaaS所需的关键技术讨论较少
面向服务的建模与仿真技术综述^[17]	2013	对SOA与HLA、DEVS、MDA和云计算等规范或技术的结合进行研究, 探讨基于SOA实现建模与仿真的服务化	1)本文同样对SOA在仿真领域的应用进行深入探讨; 2)受限于当时的技术发展, 该文缺乏对微服务、纳米服务等SOA新形态的介绍
Architectural design space for modeling and simulation as a service: A review^[18]	2020	针对MSaaS的架构设计空间进行深入研究, 给出MSaaS架构的分类标准, 指出MSaaS架构需要具备的核心能力	1)本文同样对MSaaS的架构进行探讨, 并给出通用架构; 2)该文只综述了与MSaaS架构相关的工作, 缺乏对MSaaS的相关概念、发展历程、支撑技术的讨论
Towards cloud-native simulations —— Lessons learned from the front-line of cloud computing^[19]	2021	阐述云计算范式的发展对仿真领域的影响, 提出云原生仿真参考模型, 分析微服务、纳米服务对云原生仿真的影响	1)本文同样讨论了单体服务、微服务、纳米服务等技术对云原生仿真的影响; 2)该文缺乏对实现建模与仿真服务化所需技术的整体性阐述
网络化仿真及其发展趋势^[20]	2021	给出网络化仿真的含义与特征, 阐述网络化仿真发展历程, 探讨未来网络化仿真发展趋势	1)本文同样对建模与仿真服务化的发展历程和未来趋势进行深入讨论; 2)该文对建模与仿真服务化发展历程的介绍不够全面, 也没有给出关键的支撑技术

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表 2 网页技术的发展对WBS的影响

Table 2 Impact of the development of web technology on WBS

类别	时间跨度	主要特征	仿真应用情况	优点	缺点
Web 1.0	1996 ~ 2004	用户只能“读”取网站上的内容, “写”的能力受限	应用较少, 更多的是证明可以基于网页实现仿真, 而不是需要使用网页进行仿真	简化访问; 降低对用户端设备的依赖	稳定性易受影响; 图形化能力有限; 用户对仿真的控制手段也相对匮乏
Web 2.0	2004 ~ 至今	用户间可以自由地交互, 用户既可以“读”也可以“写”	应用领域大大扩展, 并被吸纳到多种仿真标准, 如HLA Evolved	广泛访问; 支持用户通过浏览器实现仿真服务的组合与集成	难以有效管理大量服务资源; 无状态的网页服务难以保存模型状态
Web 3.0	未成熟	主要强调对用户数字资产的尊重与保护, 核心技术是区块链	还处于探索阶段	促进仿真资源的共享; 增强用户数据的隐私保护	核心的区块链技术仍面临伸缩性和吞吐率的问题; 与仿真结合的研究较少

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表 3 面向服务架构的不同实现技术的对比

Table 3 Comparison of different implementation technologies for service oriented architecture

类别	技术/标准/架构	粒度	部署策略	可移植性	自动化部署	仿真应用情况	服务状态
组件技术	CORBA、BOM、DCOM等	单体服务	虚拟机	一般	不支持	应用广泛	支持
网页服务	WSDL + SOAP + UDDI	单体服务	虚拟机	较好	支持	应用广泛	不支持
微服务	微服务架构	微服务	容器	较好	支持	发展阶段	支持
纳米服务	无服务器架构	函数	FaaS平台	一般	支持	探索阶段	不支持

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表 4 不同计算基础设施的对比

Table 4 Comparison of different computing infrastructures

类别	统一运维管理	远端访问	服务化	虚拟化	弹性扩展	使用成本	安全性
本地集群	不支持	不支持	不支持	不支持	较差	高	好
网格计算	支持	支持	支持	不支持	一般	低	一般
云计算	支持	支持	支持	支持	良好	低	一般

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参考文献(148)

[1]	Higher Education Act of 1965. As Amended Through P.L. 115-334, Enacted December 20. USA: National Education Association, 2018
[2]	李伯虎, 柴旭东, 侯宝存, 李潭, 张雅彬, 余海燕, 等. 一种基于云计算理念的网络化建模与仿真平台——“云仿真平台”. 系统仿真学报, 2009, 21(17): 5292-5299 doi: 10.16182/j.cnki.joss.2009.17.049 Li Bo-Hu, Chai Xu-Dong, Hou Bao-Cun, Li Tan, Zhang Ya-Bin, Yu Hai-Yan, et al. Networked modeling & simulation platform based on concept of cloud computing - cloud simulation platform. Journal of System Simulation, 2009, 21(17): 5292-5299 doi: 10.16182/j.cnki.joss.2009.17.049
[3]	Fujimoto R, Bock C, Chen W, Page E, Panchal J H. Research Challenges in Modeling and Simulation for Engineering Complex Systems. Berlin: Springer International Publishing, 2017.
[4]	史扬, 董汉权, 陆铭华. 面向服务的可组合可重用仿真技术研究. 系统仿真学报, 2014, 26(7): 1522-1526, 1548 doi: 10.16182/j.cnki.joss.2014.07.023 Shi Yang, Dong Han-Quan, Lu Ming-Hua. Research on simulation composability and reusability based on SOA. Journal of System Simulation, 2014, 26(7): 1522-1526, 1548 doi: 10.16182/j.cnki.joss.2014.07.023
[5]	Taylor S J E. Distributed simulation: State-of-the-art and potential for operational research. European Journal of Operational Research, 2019, 273(1): 1-19 doi: 10.1016/j.ejor.2018.04.032
[6]	Gustavsson P M, Björk Å, Brax C, Planstedt T. Towards service oriented simulations. In: Proceedings of the Fall Simulation Interoperability Workshop. Orlando, USA: Citeseer, 2004. 219−229
[7]	Li B H, Shi G Q, Lin T Y, Zhang Y X, Chai X D, Zhang L, et al. Smart simulation cloud (simulation cloud 2.0) —— The newly development of simulation cloud. In: Proceedings of the 18th Asian Simulation Conference. Kyoto, Japan: Springer, 2018. 168−185
[8]	Caglar F, Shekhar S, Gokhale A, Basu S, Rafi T, Kinnebrew J, et al. Cloud-hosted simulation-as-a-service for high school STEM education. Simulation Modelling Practice and Theory, 2015, 58: 255-273 doi: 10.1016/j.simpat.2015.06.006
[9]	Zehe D, Knoll A, Cai W T, Aydt H. SEMSim cloud service: Large-scale urban systems simulation in the cloud. Simulation Modelling Practice and Theory, 2015, 58: 157-171 doi: 10.1016/j.simpat.2015.05.005
[10]	Zhou L J, Gai X P, Lu Y, Wu P, Ren D J, Zhao C J, et al. Research and application of intelligent learning system for power grid all-element simulation based on microservice. Journal of Physics: Conference Series, 2021, 1802: Article No. 042103
[11]	Grimes J G. Department of Defense Net-Centric Services Strategy: Strategy for a Net-Centric, Service Oriented DoD Enterprise, Department of Defense, Chief Information Officer, USA, 2007
[12]	Edgren M G. Cloud-enabled modular services: A framework for cost-effective collaboration. In: Proceedings of the NATO Modelling and Simulation Group Symposium on Transforming Defence through Modelling and Simulation —— Opportunities and Challenges. Arlington, USA: NATO STO, 2012. 1−10
[13]	Hannay J E, van den Berg T. The NATO MSG-136 reference architecture for M&S as a service. In: Proceedings of the NATO Modelling and Simulation Group Symposium on M&S Technologies and Standards for Enabling Alliance Interoperability and Pervasive M&S Applications (STO-MP-MSG-149). USA: NATO Science and Technology Organization, 2017. 1−18
[14]	Siegfried D R. MSG-168 lecture series on modelling and simulationas a service (MSaaS): 3 [Online], available: https://www.sto.nato.int/publications/STO%20Educational%20Notes/STO-EN-MSG-168/EN-MSG-168-03.pdf, February 15, 2022
[15]	DOD. Defense modeling and simulation reference architecture, Version 1.0 [Online], available: https://www.msco.mil/MSReferences/Policy Guidance.aspx, May 1, 2022
[16]	Cayirci E. Modeling and simulation as a cloud service: A survey. In: Proceedings of the Winter Simulations Conference (WSC). Washington, USA: IEEE, 2013. 389−400
[17]	鞠儒生, 杨妹, 钟荣华, 刘晓铖, 周云, 黄柯棣. 面向服务的建模与仿真技术综述. 系统工程与电子技术, 2013, 35(7): 1539-1546 doi: 10.3969/j.issn.1001-506X.2013.07.13.31 Ju Ru-Sheng, Yang Mei, Zhong Rong-Hua, Liu Xiao-Cheng, Zhou Yun, Huang Ke-Di. Summary of service oriented modeling and simulation. Systems Engineering and Electronics, 2013, 35(7): 1539-1546 doi: 10.3969/j.issn.1001-506X.2013.07.13.31
[18]	Shahin M, Babar M A, Chauhan M A. Architectural design space for modelling and simulation as a service: A review. Journal of Systems and Software, 2020, 170: Article No. 110752 doi: 10.1016/j.jss.2020.110752
[19]	Kratzke N, Siegfried R. Towards cloud-native simulations – lessons learned from the front-line of cloud computing. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 2021, 18(1): 39-58 doi: 10.1177/1548512919895327
[20]	段红, 邱晓刚. 网络化仿真及其发展趋势. 系统仿真学报, 2021, 33(7): 1526-1533 doi: 10.16182/j.issn1004731x.joss.20-0032 Duan Hong, Qiu Xiao-Gang. Networked simulation and it’s development trend. Journal of System Simulation, 2021, 33(7): 1526-1533 doi: 10.16182/j.issn1004731x.joss.20-0032
[21]	Mackenzie C M, Laskey K, McCabe F, Brown P F, Metz R. Reference model for service oriented architecture 1.0 [Online], available: https://docs.oasis-open.org/soa-rm/v1.0/soa-rm.html, May 1, 2022
[22]	The Open Group. Service-oriented architecture ontology Version 2.0 [Online], available: https://publications.opengroup.org/c144, May 1, 2022
[23]	Tolk A. Engineering Principles of Combat Modeling and Distribu Simulation. Hoboken: John Wiley & Sons, 2012.
[24]	Hannay J E, van den Berg T, Gallant S, Gupton K. Modeling and simulation as a service infrastructure capabilities for discovery, composition and execution of simulation services. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 2021, 18(1): 5-28 doi: 10.1177/1548512919896855
[25]	Szyperski C. Component technology —— What, where, and how? In: Proceedings Of The 25th International Conference On Software Engineering. Portland, Usa: IeEE, 2003. 684−693
[26]	van den Berg T, Siegel B, Cramp A. Containerization of high level architecture-based simulations: A case study. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 2017, 14(2): 115-138 doi: 10.1177/1548512916662365
[27]	Liu Y, Zhang L, Liu Y K, Laili Y J, Zhang W C. Model maturity-based model service composition in cloud environments. Simulation Modelling Practice and Theory, 2021, 113: Article No. 102389 doi: 10.1016/j.simpat.2021.102389
[28]	DODD. Department of defense modeling and simulation (M&S) master plan (DoD 5000.59-P) [Online], available: https://biotech.law.lsu.edu/blaw/dodd/corres/html/500059p.htm, May 1, 2022
[29]	Liu Y, Zhang L, Zhang W C, Hu X L. An overview of simulation-oriented model reuse. In: Proceedings of the 16th Asian Simulation Conference and SCS Autumn Simulation Multi-Conference. Beijing, China: Springer, 2016. 48−56
[30]	宋莉莉. 基于 SOA 的建模与仿真框架及仿真服务发现技术研究 [博士学位论文], 国防科学技术大学, 中国, 2009 Song Li-Li. Study on SOA-Based Framework for Modeling and Simulation and the Technologies for Simulation Service Discovery [Ph.D. dissertation], National University of Defense Technology, China, 2009
[31]	Zhang L, Wang F, Li F. Cloud-based simulation. Summer of Simulation. Cham: Springer, 2019. 97−115
[32]	Fishwick P A. Web-based simulation: Some personal observations. In: Proceedings of the 28th Conference on Winter Simulation. Coronado, USA: IEEE, 1996. 772−779
[33]	Wang S X, Wainer G. Web-based simulation using Cell-DEVS modeling and GIS visualization. Modeling and Simulation-Based Systems Engineering Handbook. Boca Raton: CRC Press, 2015. 44
[34]	Wang S X, Wainer G. Modeling and simulation as a service architecture for deploying resources in the cloud. International Journal of Modeling, Simulation, and Scientific Computing, 2016, 7(1): Article No. 1641002 doi: 10.1142/S1793962316410026
[35]	Page E H, Griffin S P, Rother S L. Providing conceptual framework support for distributed Web-based simulation within the high-level architecture. In: Proceedings of SPIE 3369, Enabling Technology for Simulation Science II. Orlando, USA: SPIE, 1998. 287−292
[36]	Miller J A, Seila A F, Xiang X W. The JSIM web-based simulation environment. Future Generation Computer Systems, 2000, 17(2): 119-133 doi: 10.1016/S0167-739X(99)00108-9
[37]	Byrne J, Heavey C, Byrne P J. A review of Web-based simulation and supporting tools. Simulation Modelling Practice and Theory, 2010, 18(3): 253-276 doi: 10.1016/j.simpat.2009.09.013
[38]	史佩昌. 云服务的高效传递技术研究 [博士学位论文], 国防科学技术大学, 中国, 2012 Shi Pei-Chang. Research on Efficient Delivery Techniques for Cloud Services [Ph.D. dissertation], National University of Defense Technology, China, 2012
[39]	Kuljis J, Paul R J. A review of web based simulation: Whither we wander? In: Proceedings of the Winter Simulation Conference Proceedings. Orlando, USA: IEEE, 2000. 1872−1881
[40]	Paul R J, Taylor S J E. What use is model reuse: Is there a crook at the end of the rainbow? In: Proceedings of the Winter Simulation Conference. San Diego, USA: IEEE, 2002. 648−652
[41]	Wiedemann T. Simulation application service providing (SIM-ASP). In: Proceedings of the Winter Simulation Conference (Cat. No.01CH37304). Arlington, USA: IEEE, 2001. 623−628
[42]	Castronova A M, Goodall J L, Elag M M. Models as web services using the Open Geospatial Consortium (OGC) Web Processing Service (WPS) standard. Environmental Modelling & Software, 2013, 41: 72-83
[43]	O＇Leary P, Christon M, Jourdain S, Harris C, Berndt M, Bauer A. HPCCloud: A cloud/web-based simulation environment. In: Proceedings of the IEEE 7th International Conference on Cloud Computing Technology and Science (CloudCom). Vancouver, Canada: IEEE, 2015. 25−33
[44]	Kim D, Jeong D, Seo Y. Automated composition and execution of web-based simulation systems through knowledge designing and reasoning. Advanced Engineering Informatics, 2021, 48: Article No. 101263 doi: 10.1016/j.aei.2021.101263
[45]	Fielding R T. Architectural Styles and the Design of Network-Based Software Architectures. Irvine: University of California, 2000.
[46]	Tsai W T, Fan C, Chen Y N, Paul R. DDSOS: A dynamic distributed service-oriented simulation framework. In: Proceedings of the 39th Annual Simulation Symposium (ANSS＇06). Huntsville, USA: IEEE, 2006. 8−167
[47]	Brebner P. Service-oriented performance modeling the mule enterprise service bus (ESB) loan broker application. In: Proceedings of the 35th Euromicro Conference on Software Engineering and Advanced Applications. Patras, Greece: IEEE, 2009. 404−411
[48]	Smit M, Stroulia E. Simulating service-oriented systems: A survey and the services-aware simulation framework. IEEE Transactions on Services Computing, 2013, 6(4): 443-456 doi: 10.1109/TSC.2012.15
[49]	Arroqui M, Mateos C, Machado C, Zunino A. RESTful web Services improve the efficiency of data transfer of a whole-farm simulator accessed by Android smartphones. Computers and Electronics in Agriculture, 2012, 87: 14-18 doi: 10.1016/j.compag.2012.05.016
[50]	Al-Zoubi K, Wainer G. RISE: A general simulation interoperability middleware container. Journal of Parallel and Distributed Computing, 2013, 73(5): 580-594 doi: 10.1016/j.jpdc.2013.01.014
[51]	Morse K L, Tolk A, Pullen J M, Brutzman D. XMSF as an enabler for NATO M&S. In: Proceedings of NATO Modeling and Simulation Group Conference. Koblenz, Germany: NATO Science and Technology Organization, 2004. 1−20
[52]	钟蔚, 龚建兴, 郝建国, 黄柯棣. HLA Evolved规范研究分析. 系统仿真学报, 2011, 23(4): 691-696 Zhong Wei, Gong Jian-Xing, Hao Jian-Guo, Huang Ke-Di. Research and analysis of HLA Evolved specification. Journal of System Simulation, 2011, 23(4): 691-696
[53]	高武奇, 康凤举, 钟联炯, 傅妍芳. 一种基于HLA Evovled的云仿真技术研究. 系统仿真学报, 2011, 23(8): 1643-1647 Gao Wu-Qi, Kang Feng-Ju, Zhong Lian-Jiong, Fu Yan-Fang. Cloud simulation technology based on HLA Evolved. Journal of System Simulation, 2011, 23(8): 1643-1647
[54]	何强, 郝建国, 黄健. 基于SOA的仿真服务系统. 计算机仿真, 2007, 24(5): 98-102 doi: 10.3969/j.issn.1006-9348.2007.05.028 He Qiang, Hao Jian-Guo, Huang Jian. A simulation service system based on SOA. Computer Simulation, 2007, 24(5): 98-102 doi: 10.3969/j.issn.1006-9348.2007.05.028
[55]	Zeldman J. Web 3.0: A list apart [Online], available: https://alistapart.com/article/web3point0/, May 1, 2022
[56]	Miller J A, Baramidze G. Simulation and the semantic Web. In: Proceedings of the Winter Simulation Conference. Orlando, USA: IEEE, 2005. 2371−2377
[57]	Zhang T, Liu Y S, Zha Y B. Semantic web based simulation service customization and composition. In: Proceedings of the 7th IEEE International Conference on Computer and Information Technology (CIT 2007). Aizu-Wakamatsu, Japan: IEEE, 2007. 235−240
[58]	Bell D, de Cesare S, Lycett M, Mustafee N, Taylor S J E. Semantic web service architecture for simulation model reuse. In: Proceedings of the 11th IEEE International Symposium on Distributed Simulation and Real-Time Applications (DS-RT＇07). Chania, Greece: IEEE, 2007. 129−136
[59]	Erl T. Service-Oriented Architecture: Concepts, Technology, and Design. New Jersey: Prentice Hall PTR, 2005.
[60]	Davis P K, Tolk A. Observations on new developments in composability and multi-resolution modeling. In: Proceedings of the Winter Simulation Conference. Washington, USA: IEEE, 2007. 859−870
[61]	Hofmann M A. Criteria for decomposing systems into components in modeling and simulation: Lessons learned with military simulations. Simulation, 2004, 80(7-8): 357-365 doi: 10.1177/0037549704049876
[62]	Gustavson P, Chase T, Root L, Crosson K. Moving towards a service-oriented architecture (SOA) for distributed component simulation environments. In: Proceedings of the Spring Simulation Interoperability Workshop. Orlando, USA: IEEE, 2005. 1−8
[63]	Mittal S, Zeigler B P, Martín J L R, Sahin F, Jamshidi B S M. Modeling and simulation for systems of systems engineering. System of Systems Engineering: Innovations for the 21st Century. Hoboken: Wiley, 2009. 101−149
[64]	Hu J P, Huang L P, Cao B, Chang X L. Executable modeling approach to service oriented architecture using SoaML in conjunction with extended DEVSML. In: Proceedings of the IEEE International Conference on Services Computing. Anchorage, USA: IEEE, 2014. 243−250
[65]	Ramaswamy M V. System Theory Based Modeling and Simulation of SOA-Based Software Systems [Master thesis], Arizona State University, USA, 2008
[66]	Sarjoughian H, Kim S, Ramaswamy M, Yau S. A simulation framework for service-oriented computing systems. In: Proceedings of the Winter Simulation Conference. Miami, USA: IEEE, 2008. 845−853
[67]	Park A J. Master/Worker Parallel Discrete Event Simulation [Ph.D. dissertation], Georgia Institute of Technology, USA, 2008
[68]	Lewis J, Fowler M. Microservices [Online], available: https://martinfowler.com/articles/microservices.html, May 1, 2022
[69]	Villamizar M, Garcés O, Castro H, Verano M, Salamance L, Casallas R, et al. Evaluating the monolithic and the microservice architecture pattern to deploy web applications in the cloud. In: Proceedings of the 10th Computing Colombian Conference (10CCC). Bogota, Colombia: IEEE, 2015. 583−590
[70]	Namiot D, Sneps-Sneppe M. On micro-services architecture. International Journal of Open Information Technologies, 2014, 2(9): 24-27
[71]	Taylor S J E, Anagnostou A, Kiss T, Pattison G, Kite S, Kovacs J, et al. An architecture for an autoscaling cloud-based system for simulation experimentation. In: Proceedings of the Winter Simulation Conference. Gothenburg, Sweden: IEEE, 2018. 4088−4089
[72]	Abubakar N T, Taylor S J E, Anagnostou A. Cloud-based modeling & simulation: Introducing the distributed simulation layer. In: Proceedings of the Winter Simulation Conference. Gothenburg, Sweden: IEEE, 2018. 4218−4219
[73]	刘永奎, 曾鸣, 张霖, 郭金维, 原思阳, 平垚垚. 基于微服务架构的云制造调度仿真系统设计与开发. 系统仿真学报, 2022, 34(4): 700-711 doi: 10.16182/j.issn1004731x.joss.21-1017 Liu Yong-Kui, Zeng Ming, Zhang Lin, Guo Jin-Wei, Yuan Si-Yang, Ping Yao-Yao. Design and development of a simulation system for scheduling in cloud manufacturing based on microservice architecture. Journal of System Simulation, 2022, 34(4): 700-711 doi: 10.16182/j.issn1004731x.joss.21-1017
[74]	Kecskemeti G, Marosi A C, Kertesz A. The ENTICE approach to decompose monolithic services into microservices. In: Proceedings of the International Conference on High Performance Computing & Simulation (HPCS). Innsbruck, Austria: IEEE, 2016. 591−596
[75]	Weinman J. Mathematical proof of the inevitability of cloud computing [Online], available: http://asecib.ase.ro/cc/articole/Inevitability%20of%20Cloud.pdf, May 1, 2022
[76]	Baldini I, Castro P, Chang K, Cheng P, Fink S, Ishakian V, et al. Serverless computing: Current trends and open problems. Research Advances in Cloud Computing. Singapore: Springer, 2017. 1−20
[77]	Kritikos K, Skrzypek P. Simulation-as-a-service with serverless computing. In: Proceedings of the IEEE World Congress on Services (SERVICES). Milan, Italy: IEEE, 2019. 200−205
[78]	Kratzke N, Quint P C, Palme D, Reimers D. Project cloud TRANSIT or to simplify cloud-native application provisioning for SMEs by integrating already available container technologies. In: Proceedings of the European Space Project on Smart Systems, Big Data, Future Internet —— Towards Serving the Grand Societal Challenges. Rome, Italy: SciTePress, 2016. 3−26
[79]	Villamizar M, Garcés O, Ochoa L, Castro H, Salamanca L, Verano M, et al. Cost comparison of running web applications in the cloud using monolithic, microservice, and AWS Lambda architectures. Service Oriented Computing and Applications, 2017, 11(2): 233-247 doi: 10.1007/s11761-017-0208-y
[80]	Hellerstein J M, Faleiro J, Gonzalez J E, Schleier-Smith J, Sreekanti V, Tumanov A, et al. Serverless computing: One step forward, two steps back. arXiv preprint arXiv: 1812.03651, 2018.
[81]	Rycerz K, Bubak M, Malawski M, Sloot P. Support for effective and fault tolerant execution of HLA-based applications in the OGSA framework. In: Proceedings of the 4th International Conference on Computational Science. Kraków, Poland: Springer, 2004. 848−855
[82]	Xie Y, Teo Y M, Cai W, Turner S J. Towards grid-wide modeling and simulation. In: Proceedings of the Singapore-MIT Alliance Annual Symposium. Singapore: Singapore-MIT Alliance, 2005. 1−9
[83]	Xie Y, Teo Y M, Cai W, Turner S J. Service provisioning for HLA-based distributed simulation on the grid. In: Proceedings of the Workshop on Principles of Advanced and Distributed Simulation (PADS＇05). Monterey, USA: IEEE, 2005. 282−291
[84]	Chen X J, Cai W T, Turner S J, Wang Y. SOAr-DSGrid: Service-oriented architecture for distributed simulation on the grid. In: Proceedings of the 20th Workshop on Principles of Advanced and Distributed Simulation (PADS＇06). Singapore: IEEE, 2006. 65−73
[85]	Li B H, Chai X D, Di Y Q, Yu H Y, Du Z H, Peng X Y. Research on service oriented simulation grid. In: Proceedings of the Autonomous Decentralized Systems. Chengdu, China: IEEE, 2005. 7−14
[86]	张卫. 面向并行分布式仿真的服务网格关键技术研究 [博士学位论文], 国防科学技术大学, 中国, 2009 Zhang Wei. Research on Key Technologies of Service Grid for Parallel and Distributed Simulations [Ph.D. dissertation], National University of Defense Technology, China, 2009
[87]	蔡楹. 面向服务的仿真支持环境关键技术研究 [博士学位论文], 国防科学技术大学, 中国, 2014 Cai Ying. Research on Key Technologies of Service-Oriented Simulation Supporting Environment [Ph.D. dissertation], National University of Defense Technology, China, 2014
[88]	Risco-Martín J L, Henares K, Mittal S, Almendras L F, Olcoz K. A unified cloud-enabled discrete event parallel and distributed simulation architecture. Simulation Modelling Practice and Theory, 2022, 118: Article No. 102539 doi: 10.1016/j.simpat.2022.102539
[89]	Bordón-Ruiz J, Besada-Portas E, López-Orozco J A. Cloud DEVS-based computation of UAVs trajectories for search and rescue missions. Journal of Simulation, 2022, 16(6): 572-588 doi: 10.1080/17477778.2022.2053311
[90]	Matlekovic L, Juric F, Schneider-Kamp P. Microservices for autonomous UAV inspection with UAV simulation as a service. Simulation Modelling Practice and Theory, 2022, 119: Article No. 102548 doi: 10.1016/j.simpat.2022.102548
[91]	Grom A, Rheinsmith R, Blount E, Janele J. Joint staff J7 joint training tools for campaign planning. In: Proceedings of the MODSIM World Conference. Virginia Beach, USA: IEEE, 2017. 1−10
[92]	VR360. New British Army programme set to use VR, MR, and cloud software [Online], available: https://virtualreality-news.net/news/2019/feb/05/new-british-army-programme-set-use-vr-mr-and-cloud-software/, May 1, 2022
[93]	Fujimoto R M, Malik A W, Park A J. Parallel and distributed simulation in the cloud. SCS M&S Magazine, 2010, 3: 1-10
[94]	Tolk A. Composability challenges for effective cyber physical systems applications in the domain of cloud, edge, and fog computing. Simulation for Cyber-Physical Systems Engineering. Cham: Springer, 2020. 25−42
[95]	Stine M. Migrating to Cloud-native Application Architectures. Sebastopol: O＇Reilly Media, 2015.
[96]	Liu X C, He Q, Qiu X G, Chen B, Huang K E. Cloud-based computer simulation: Towards planting existing simulation software into the cloud. Simulation Modelling Practice and Theory, 2012, 26: 135-150 doi: 10.1016/j.simpat.2012.05.001
[97]	Shekhar S, Abdel-Aziz H, Walker M, Caglar F, Gokhale A, Koutsoukos X. A simulation as a service cloud middleware. Annals of Telecommunications, 2016, 71(3-4): 93-108 doi: 10.1007/s12243-015-0475-6
[98]	Cayirci E, Karapinar H, Ozcakir L. Joint military space operations simulation as a service. In: Proceedings of the Winter Simulation Conference (WSC). Las Vegas, USA: IEEE, 2017. 4129−4140
[99]	王会霞, 陈宜成, 谭浪, 柳嘉润. 基于云平台的体系化仿真技术研究. 控制与信息技术, 2018(6): 100-103, 108 Wang Hui-Xia, Chen Yi-Cheng, Tan Lang, Liu Jia-Run. Research on system of system simulation technology based on cloud platform. Control and Information Technology, 2018(6): 100-103, 108
[100]	齐和平, 丁玮, 王学文, 田川, 侯海宏. 基于云架构的一体化联合训练仿真体系. 火力与指挥控制, 2019, 44(4): 69-73 doi: 10.3969/j.issn.1002-0640.2019.04.014 Qi He-Ping, Ding Wei, Wang Xue-Wen, Tian Chuan, Hou Hai-Hong. Analysis and discussion about simulation system of integrated joint operational training based on cloud architecture. Fire Control & Command Control, 2019, 44(4): 69-73 doi: 10.3969/j.issn.1002-0640.2019.04.014
[101]	Çayirci E, Marincic D. Computer Assisted Exercises and Training: A Reference Guide. Hoboken: John Wiley & Sons, 2009.
[102]	Zhang Y H, Qu P, Cihang J, Zheng W M. A cloud gaming system based on user-level virtualization and its resource scheduling. IEEE Transactions on Parallel and Distributed Systems, 2015, 27(5): 1239-1252
[103]	Zhang X, Chen H, Zhao Y C, Ma Z, Xu Y L, Huang H J, et al. Improving cloud gaming experience through mobile edge computing. IEEE Wireless Communications, 2019, 26(4): 178-183 doi: 10.1109/MWC.2019.1800440
[104]	Bymer M L. DSTS: First immersive virtual training system fielded [Online], available: https://www.army.mil/article/84728/dsts_first_immersive_virtual_training_system_fielded, May 1, 2022
[105]	Li B H, Chai X D, Lin T Y, Yang C, Hou B C, Liu Y, et al. Cyber-physical system engineering oriented intelligent high performance simulation cloud. Simulation for Cyber-Physical Systems Engineering. Cham: Springer, 2020. 89−118
[106]	边缘计算产业联盟. 边缘计算与云计算协同白皮书 2.0 [Online], available: http://www.ecconsortium.org/Lists/show/id/522.html, 2020 Edge Computing Consortium. Edge computing and cloud computing collaboration white paper 2.0 [Online], available: http://www.ecconsortium.org/Lists/show/id/522.html, May 1, 2022
[107]	Chang S, Hood R, Jin H, Heistand S, Chang J, Cheung S, et al. Evaluating the Suitability of Commercial Clouds for NASA＇s High Performance Computing Applications: A Trade Study, NAS Technical Report NAS-2018-01, NASA Ames Research Center, USA, 2018
[108]	杜楠, 谭亚新, 冯斌. 基于对象元模型的LVC实验资源服务化方法研究. 系统仿真学报, 2022, 34(8): 1834-1846 doi: 10.16182/j.issn1004731x.joss.21-0308 Du Nan, Tan Ya-Xin, Feng Bin. Servicing method of LVC experiment resources based on object metamodel. Journal of System Simulation, 2022, 34(8): 1834-1846 doi: 10.16182/j.issn1004731x.joss.21-0308
[109]	Achir M, Abdelli A, Mokdad L, Benothman J. Service discovery and selection in IoT: A survey and a taxonomy. Journal of Network and Computer Applications, 2022, 200: Article No. 103331 doi: 10.1016/j.jnca.2021.103331
[110]	Huang Z, Zhao W. A semantic matching approach addressing multidimensional representations for web service discovery. Expert Systems with Applications, 2022, 210: Article No. 118468 doi: 10.1016/j.eswa.2022.118468
[111]	Al-Sayed M M, Hassan H A, Omara F A. An intelligent cloud service discovery framework. Future Generation Computer Systems, 2020, 106: 438-466 doi: 10.1016/j.future.2019.12.027
[112]	NATO STO. Modelling and Simulation as a Service, Volume 2: Discovery Service and Metadata, The NATO Science and Technology Organization, USA, 2019
[113]	NATO STO. Modelling and Simulation as a Service, Volume 4: Experimentation Report, The NATO Science and Technology Organization, USA, 2019
[114]	刘营, 张霖, 赖李媛君. 复杂系统仿真的模型重用研究. 中国科学: 信息科学, 2018, 48(7): 743-766 doi: 10.1360/N112017-00272 Liu Ying, Zhang Lin, Laili Yuan-Jun. Study on model reuse for complex system simulation. SCIENTIA SINICA Informationis, 2018, 48(7): 743-766 doi: 10.1360/N112017-00272
[115]	Page E H, Briggs R, Tufarolo J A. Toward a family of maturity models for the simulation interconnection problem. In: Proceedings of the Spring Simulation Interoperability Workshop. Los Alamitos, USA: IEEE, 2004. 1−11
[116]	Tolk A, Muguira J A. The levels of conceptual interoperability model (LCIM). In: Proceedings of the Fall Simulation Interoperability Workshop. Orlando, USA: Simulation Interoperability Standards Organization (SISO), 2003. 1−11
[117]	Tolk A, Bair L J, Diallo S Y. Supporting network enabled capability by extending the levels of conceptual interoperability model to an interoperability maturity model. The Journal of Defense Modeling and Simulation: Applications, Methodology, Technology, 2013, 10(2): 145-160 doi: 10.1177/1548512911428457
[118]	Wang W, Tolk A, Wang W. The levels of conceptual interoperability model: Applying systems engineering principles to M&S. In: Proceedings of the Spring Simulation Multiconference. San Diego, USA: IEEE, 2009. 1−9
[119]	Zhao D, Zhou Z B, Hung P C K, Deng S G, Xue X, Gaaloul W. CTL-based adaptive service composition in edge networks. IEEE Transactions on Services Computing, 2023, 16(2): 1051−1065
[120]	Deshpande N, Sharma N, Yu Q, Krutz D E. R-CASS: Using algorithm selection for self-adaptive service oriented systems. In: Proceedings of the IEEE International Conference on Web Services (ICWS). Chicago, USA: IEEE, 2021. 61−72
[121]	NATO STO. Modelling and Simulation as a Service —— Rapid Deployment of Interoperable and Credible Simulation Environments, MSG-136, The NATO Science and Technology Organization, USA, 2018
[122]	张霖, 陆涵. 从建模仿真看数字孪生. 系统仿真学报, 2021, 33(5): 995-1007 doi: 10.16182/j.issn1004731x.joss.21-0262 Zhang Lin, Lu Han. Discussing digital twin from of modeling and simulation. Journal of System Simulation, 2021, 33(5): 995-1007 doi: 10.16182/j.issn1004731x.joss.21-0262
[123]	朱锐. 可信服务组合若干关键技术研究 [博士学位论文], 国防科学技术大学, 中国, 2009 Zhu Rui. Research on Key Technologies for Trustworthy Service Composition [Ph.D. dissertation], National University of Defense Technology, China, 2009
[124]	李伟, 张欢, 马萍, 杨明. 云仿真系统可信度评估问题探讨. 系统仿真学报, 2022, 34(4): 679-687 doi: 10.16182/j.issn1004731x.joss.21-1170 Li Wei, Zhang Huan, Ma Ping, Yang Ming. Research on credibility assessment of cloud simulation system. Journal of System Simulation, 2022, 34(4): 679-687 doi: 10.16182/j.issn1004731x.joss.21-1170
[125]	Walker E. Benchmarking Amazon EC2 for high-performance scientific computing. LOGIN, 2008, 33(5): 18-23
[126]	Jackson K R, Ramakrishnan L, Muriki K, Canon S, Cholia S, Shalf J, et al. Performance analysis of high performance computing applications on the Amazon web services cloud. In: Proceedings of the IEEE Second International Conference on Cloud Computing Technology and Science. Indianapolis, USA: IEEE, 2010. 159−168
[127]	Sianati A, Boukerche A, De Grande R. Bundling communication messages in large scale cloud environments. In: Proceedings of the IEEE Symposium on Computers and Communication (ISCC). Larnaca, Cyprus: IEEE, 2015. 788−795
[128]	D’Angelo G, Marzolla M. New trends in parallel and distributed simulation: From many-cores to cloud computing. Simulation Modelling Practice and Theory, 2014, 49: 320-335 doi: 10.1016/j.simpat.2014.06.007
[129]	Malik A, Park A, Fujimoto R. Optimistic synchronization of parallel simulations in cloud computing environments. In: Proceedings of the IEEE International Conference on Cloud Computing. Bangalore, India: IEEE, 2009. 49−56
[130]	Bauer P, Lindén J, Engblom S, Jonsson B. Efficient inter-process synchronization for parallel discrete event simulation on multicores. In: Proceedings of the 3rd ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. London, United Kingdom: ACM, 2015. 183−194
[131]	Li Z X, Cai W T, Turner S J, Li X R, Duong T N B, Goh R S M. Adaptive resource provisioning mechanism in VEEs for improving performance of HLA-based simulations. ACM Transactions on Modeling and Computer Simulation, 2015, 26(1): Article No. 1
[132]	Vanmechelen K, De Munck S, Broeckhove J. Conservative distributed discrete-event simulation on the Amazon EC2 cloud: An evaluation of time synchronization protocol performance and cost efficiency. Simulation Modelling Practice and Theory, 2013, 34: 126-143 doi: 10.1016/j.simpat.2013.02.002
[133]	Mofrad M H, Melhem R, Hammoud M. Revolver: Vertex-centric graph partitioning using reinforcement learning. In: Proceedings of the IEEE 11th International Conference on Cloud Computing (CLOUD). San Francisco, USA: IEEE, 2018. 818−821
[134]	Dindokar R, Simmhan Y. Adaptive partition migration for irregular graph algorithms on elastic resources. In: Proceedings of the IEEE 12th International Conference on Cloud Computing (CLOUD). Milan, Italy: IEEE, 2019. 281−290
[135]	Hosseinalipour S, Nayak A, Dai H Y. Power-aware allocation of graph jobs in geo-distributed cloud networks. IEEE Transactions on Parallel and Distributed Systems, 2020, 31(4): 749-765 doi: 10.1109/TPDS.2019.2943457
[136]	Zhou A, Wang S G, Ma X, Yau S S. Towards service composition aware virtual machine migration approach in the cloud. IEEE Transactions on Services Computing, 2020, 13(4): 735-744 doi: 10.1109/TSC.2019.2962128
[137]	Bao L, Wu C S, Bu X X, Ren N N, Shen M Q. Performance modeling and workflow scheduling of microservice-based applications in clouds. IEEE Transactions on Parallel and Distributed Systems, 2019, 30(9): 2114-2129 doi: 10.1109/TPDS.2019.2901467
[138]	Wang S, Zhu F, Yao Y P, Tang W J, Xiao Y H, Xiong S Q. A computing resources prediction approach based on ensemble learning for complex system simulation in cloud environment. Simulation Modelling Practice and Theory, 2021, 107: Article No. 102202 doi: 10.1016/j.simpat.2020.102202
[139]	Xiao Y H, Yao Y P, Chen K, Tang W J, Zhu F. A simulation task partition method based on cloud computing resource prediction using ensemble learning. Simulation Modelling Practice and Theory, 2022, 119: Article No. 102595 doi: 10.1016/j.simpat.2022.102595
[140]	Mikida E, Kale L. Adaptive methods for irregular parallel discrete event simulation workloads. In: Proceedings of the ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. Rome, Italy: ACM, 2018. 189−200
[141]	Alkharboush R, De Grande R E, Boukerche A. Load prediction in HLA-based distributed simulation using Holt＇s variants. In: Proceedings of the IEEE/ACM 17th International Symposium on Distributed Simulation and Real Time Applications. Delft, Netherlands: IEEE, 2013. 161−168
[142]	De Grande R E, Boukerche A, Alkharboush R. Time series-oriented load prediction model and migration policies for distributed simulation systems. IEEE Transactions on Parallel and Distributed Systems, 2017, 28(1): 215-229 doi: 10.1109/TPDS.2016.2552174
[143]	Tang W J, Yao Y P, Li T L, Song X, Zhu F. An adaptive persistence and work-stealing combined algorithm for load balancing on parallel discrete event simulation. ACM Transactions on Modeling and Computer Simulation, 2020, 30(2): Article No. 12
[144]	Lindén J, Bauer P, Engblom S, Jonsson B. Fine-grained local dynamic load balancing in PDES. In: Proceedings of the ACM SIGSIM Conference on Principles of Advanced Discrete Simulation. Rome, Italy: ACM, 2018. 201−212
[145]	Che Z S, Zhao C, Laili Y J, Zhang L. Research on the dynamic management of cloud simulation derived data. International Journal of Modeling, Simulation, and Scientific Computing, 2017, 8(3): Article No. 1750026 doi: 10.1142/S179396231750026X
[146]	Liu X Z, Sun S X, Huang G. Decentralized services computing paradigm for blockchain-based data governance: Programmability, interoperability, and intelligence. IEEE Transactions on Services Computing, 2020, 13(2): 343-355
[147]	李伯虎, 柴旭东, 张霖, 卿杜政, 施国强, 林廷宇, 等. 面向智慧物联网的新型嵌入式仿真技术研究. 系统仿真学报, 2022, 34(3): 419-441 doi: 10.16182/j.issn1004731x.joss.22-0119 Li Bo-Hu, Chai Xu-Dong, Zhang Lin, Qing Du-Zheng, Shi Guo-Qiang, Lin Ting-Yu, et al. New embedded simulation technology for smart internet of things. Journal of System Simulation, 2022, 34(3): 419-441 doi: 10.16182/j.issn1004731x.joss.22-0119
[148]	邸彦强, 李婷, 冯少冲, 刘琼瑶, 吕建红, 陈志佳, 等. 云边端架构的装备精确维修平行仿真系统. 系统仿真学报, 2022, 34(9): 1909-1919 doi: 10.16182/j.issn1004731x.joss.22-0220 Di Yan-Qiang, Li Ting, Feng Shao-Chong, Liu Qiong-Yao, Lü Jian-Hong, Chen Zhi-Jia, et al. Parallel simulation system of equipment precision maintenance based on cloud-edge-end architecture. Journal of System Simulation, 2022, 34(9): 1909-1919 doi: 10.16182/j.issn1004731x.joss.22-0220