基于SAAS的LVC装备试验资源实时调度模型研究

doi:10.12305/j.issn.1001-506X.2023.12.22

Abstract

Abstract:

Live-virtual-construction (LVC) simulation provides effective technical means for equipment system test. In order to solve the problem that the interaction between a large number of heterogeneous simulation resource objects in the test could not meet the real-time demand of the test task, the real-time scheduling model of simulation resources is researched. The functional components of the model are analyzed, and the scheduling model is divided into three sub-models: remote method communication implementation, remote method cloud service, and optimized scheduling strategy. The functions of each sub-model and their relationships are analyzed, the operation steps and implementation principles of the model are given. A standard experimental environment is constructed to verify the model algorithm. The experiment shows that under the standard test environment where the delay requirement of the model is less than 1 ms, the cloud service environment can bear the subscription requirement of about 30 nodes at most at the same time, and the scheduling success rate is all more than 98.5% and the packet loss rate is 0, which meets the performance requirements of the model.

Key words: simulation as a service (SAAS), live-virtual-constructive (LVC), equipment test resource, real-time scheduling model

CLC Number:

E117

Nan DU, Wencheng PENG, Yaxin TAN. Research on real-time scheduling model of LVC equipment test resources based on SAAS[J]. Systems Engineering and Electronics, 2023, 45(12): 3924-3931.

Figures/Tables 9

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References 32

1	王鹏. 虚实结合的武器装备试验方法的若干技术研究[D]. 长沙: 国防科技大学, 2018.
	WANG P. Research on some technologies of virtual and real integrated equipment test[D]. Changsha: National University of Defense Technology, 2018.
2	郭齐胜, 董志明. 试验训练一体化仿真支撑平台设计研究[J]. 计算机仿真, 2020, 37 (9): 1- 5. doi: 10.3969/j.issn.1006-9348.2020.09.001
	GUO Q S , DONG Z M . Research on design of test and training integrated simulation supporting platform[J]. Computer Simulation, 2020, 37 (9): 1- 5. doi: 10.3969/j.issn.1006-9348.2020.09.001
3	董志明, 郭齐胜. 试验训练一体化仿真支撑技术研究[J]. 计算机仿真, 2021, 38 (2): 1-3, 23. doi: 10.3969/j.issn.1006-9348.2021.02.002
	DONG Z M , GUO Q S . Research on support technology of test and training integrated simulation[J]. Computer Simulation, 2021, 38 (2): 1-3, 23. doi: 10.3969/j.issn.1006-9348.2021.02.002
4	杨晓岚, 陈奡, 张翠侠, 等. 基于LVC的试验鉴定支撑平台构建方法研究[C]//第六届中国指挥控制大会论文集, 2018: 534-536.
	YANG X L, CHEN A, ZHANG C X, et al. Research on construction method of test support platform based on LVC[C]//Proc. of the 6th China Command and Control Conference, 2018: 534-536.
5	白爽, 洪俊. 美军面向LVC联合训练的技术发展[J]. 指挥控制与仿真, 2020, 42 (5): 135- 140.
	BAI S , HONG J . Development of U.S.LVC joint training technology[J]. Command Control & Simulation, 2020, 42 (5): 135- 140.
6	王晓路, 贾长伟, 刘闻, 等. 体系级LVC仿真集成技术研究[C]// 2020中国仿真大会, 2020: 358-364.
	WANG X L, JIA C W, LIU W, et al. Research on system level LVC simulation integration technology[C]//Proc. of the 2020 China Simulation Conference, 2020: 358-364.
7	彭健, 赵雯, 章乐平, 等. 虚拟试验支撑框架VITA研究与实现[J]. 计算机测量与控制, 2017, 25 (8): 289- 293.
	PENG J , ZHAO W , ZHANG Y P , et al. Research and Implementation of virtual test and evaluation enabling architecture[J]. Computer Measurement & Control, 2017, 25 (8): 289- 293.
8	DUNNE R , HARRIS S , REINERMAN L , et al. Validating simulators for live, virtual, constructive exercises: a methodology[J]. Applied Sciences, 2020, 10 (12): 4361. doi: 10.3390/app10124361
9	林伟伟, 齐德昱. 云计算资源调度研究综述[J]. 计算机科学, 2012, 39 (10): 1- 6. doi: 10.3969/j.issn.1002-137X.2012.10.001
	LIN W W , QI D Y . Survey of resource scheduling in cloud computing[J]. Computer Science, 2012, 39 (10): 1- 6. doi: 10.3969/j.issn.1002-137X.2012.10.001
10	左利云, 曹志波. 云计算中调度问题研究综述[J]. 计算机应用研究, 2012, 29 (11): 4023- 4027. doi: 10.3969/j.issn.1001-3695.2012.11.005
	ZUO L Y , CAO Z B . Review of scheduling research in cloud computing[J]. Application Research of Computers, 2012, 29 (11): 4023- 4027. doi: 10.3969/j.issn.1001-3695.2012.11.005
11	王西宝. 武器装备联合仿真试验关键技术研究[D]. 长沙: 国防科技大学, 2017.
	WANG X B. Research on the key technologies of joint simulation and experiment on weapon equipment[D]. Changsha: National University of Defense Technology, 2017.
12	CAYIRCI E. Modeling and simulation as a cloud service: a survey[C]//Proc. of the Winter Simulations Conference, 2013: 389-400.
13	HANNAY J E , VAN D B T , GALLANT S , et al. Modeling and simulation as a service infrastructure capabilities for disco-very, composition and execution of simulation services[J]. The Journal of Defense Modeling and Simulation, 2021, 18 (1): 5- 28. doi: 10.1177/1548512919896855
14	SIEGFRIED R . Simulation for cyber-physical systems engineering[M]. Switzerland: Springer, 2020.
15	殷凡, 牛丽. 基于云计算资源调度的多Agent遗传算法[J]. 科技通报, 2014, 30 (12): 247-249, 252. doi: 10.3969/j.issn.1001-7119.2014.12.083
	YIN F , NIU L . Algorithm of multi-agent genetic based on cloud computing resource scheduling[J]. Bulletin of Science and Technology, 2014, 30 (12): 247-249, 252. doi: 10.3969/j.issn.1001-7119.2014.12.083
16	邓志龙, 张琦玮, 曹皓, 等. 一种基于深度强化学习的调度优化方法[J]. 西北工业大学学报, 2017, 35 (6): 1047- 1053. doi: 10.3969/j.issn.1000-2758.2017.06.017
	DENG Z L , ZHANG Q W , CAO H , et al. A scheduling optimization method based on deep reinforcement learning[J]. Journal of Northwestern Polytechnical University, 2017, 35 (6): 1047- 1053. doi: 10.3969/j.issn.1000-2758.2017.06.017
17	SIEGFRIED R , VAN D B T . M&S as a service: paradigm for future simulation environments[J]. Applied Sciences, 2015, 4 (10): 22- 30.
18	SIEGFRIED R. Modeling and simulation as a service[R]. London: Sage Publications, 2021, 18(1): 98-107.
19	冯润明, 王国玉, 黄柯棣. TENA中间件的设计与实现[J]. 系统仿真学报, 2004, 6 (11): 2373- 2377. doi: 10.3969/j.issn.1004-731X.2004.11.001
	FENG R M , WANG G Y , HUANG K D . The design and implementation of TENA middleware[J]. Journal of System Simulation, 2004, 6 (11): 2373- 2377. doi: 10.3969/j.issn.1004-731X.2004.11.001
20	卫翔, 范学满, 于德新. 面向服务的仿真实时中间件系统研究[J]. 计算机仿真, 2019, 36 (7): 257-260, 307. doi: 10.3969/j.issn.1006-9348.2019.07.054
	WEI X , FAN X M , YU D X . Research on the service-oriented simulation real-time middleware system[J]. Computer Simulation, 2019, 36 (7): 257-260, 307. doi: 10.3969/j.issn.1006-9348.2019.07.054
21	SIEGFRIED R , VAN D B G T , CRAMP A , et al. M&S as a service: expectations and challenges[M]. Orlando: Siso, 2014: 40- 51.
22	DE L P F , RODRÍGUEZ-GONZÁLEZ S , CHAMOSO P , et al. An intelligent approach to allocating resources within an agent-based cloud computing platform[J]. Applied Sciences, 2020, 10 (12): 4361. doi: 10.3390/app10124361
23	SHADI A , MANISHA M . Impacts and challenges of cloud business intelligence[M]. Hershey: IGI Global, 2021.
24	HUSSAIN A , CHUN J , KHAN M . A novel framework towards viable cloud service selection as a service (CSSAAS) under a fuzzy environment[J]. Future Generation Computer Systems, 2020, 104 (3): 74- 91.
25	CHO S , HWANG S , SHIN W , et al. Design of military service framework for enabling migration to military SAAS cloud environment[J]. Electronics, 2021, 10 (5): 572. doi: 10.3390/electronics10050572
26	朱伟, 程飞, 王新. 基于云计算的存储资源管理调度技术研究[J]. 中国电子科学研究院学报, 2020, 15 (6): 513- 517.
	ZHU W , CHENG F , WANG X . Research on storage resource management and scheduling technology based on cloud computing[J]. Journal of China Academy of Electronics and Information Technology, 2020, 15 (6): 513- 517.
27	GEORGE S S , PRAMILA R S . A review of different techniques in cloud computing[J]. Materials Today: Proceedings, 2021, 5 (6): 99- 108.
28	FELLIR F, ELATTAR A, NAFIL K, et al. A multi-agent based model for task scheduling in cloud-fog computing platform[C]//Proc. of the IEEE International Conference on Informatics, IOT, and Enabling Technologies, 2020: 377-382.
29	CHANDAK A V, RAY N K. Multi agent based resource provisioning in fog computing[C]//Proc. of the International Conference on Computational Intelligence, Security and Internet of Things, 2020: 317-327.
30	PARMAR A , MEHTA R . An extensive survey on resource allocation mechanisms in cloud computing[J]. Computer, 2020, 32 (7): 58- 66.
31	周晓晨. 面向服务的云仿真平台资源调度管理[D]. 太原: 中北大学, 2020.
	ZHOU X C. Service-oriented cloud simulation platform resource management[D]. Taiyuan: North University Of China, 2020.
32	林伟伟, 朱朝悦. 面向大规模云资源调度的可扩展分布式调度方法[J]. 计算机工程与科学, 2015, 37 (11): 1997- 2005.
	LIN W W , ZHU C Y . A scalable distributed scheduling method for large-scale cloud resources[J]. Computer Engineering & Science, 2015, 37 (11): 1997- 2005.

参数	订阅需求节点数
参数	1	10	30	40
最大值/ms	0.609	0.903	1.370	1.862
最小值/ms	0.338	0.566	0.814	1.149
平均值/ms	0.415	0.699	0.926	1.237
成功率/%	1.000	0.988	1.000	0.989
丢包率/%	0.000	0.000	0.000	0.000

Research on real-time scheduling model of LVC equipment test resources based on SAAS

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