A highly reliable embedding algorithm for airborne tactical network virtualization

doi:10.23919/JSEE.2021.000116

Journal of Systems Engineering and Electronics ›› 2021, Vol. 32 ›› Issue (6): 1364-1374.doi: 10.23919/JSEE.2021.000116

• ELECTRONICS TECHNOLOGY • Previous Articles Next Articles

A highly reliable embedding algorithm for airborne tactical network virtualization

Jingcheng MIAO¹(), Na LYU^1,*(), Kefan CHEN²(), Zhuo CHEN¹(), Weiting GAO¹()

¹ College of Information and Navigation, Air Force Engineering University, Xi’an 710077, China
² Unit 94860 of the PLA, Nanjing 210000, China

Received:2020-05-21 Online:2022-01-05 Published:2022-01-05
Contact: Na LYU E-mail:zhongdcz@163.com;lvnn2007@163.com;1148180199@qq.com;408258313@qq.com;519105941@qq.com
About author:|MIAO Jingcheng was born in 1995. He received his B.S. and M.S. degrees from Engineering University of PAP in 2016 and 2018, respectively. He is currently pursuing his Ph.D. degree in Air Force Engineering University. His research interests include airborne tactial networks, network virtualization, and virtual network embedding. E-mail: zhongdcz@163.com||LYU Na was born in 1970. She received her B.S., M.S., and Ph.D. degrees from Northwestern Polytechnical University in 1992, 1995, and 2010, respectively. She is currently a full professor in Air Force Engineering University. Her research interests include aviation data link systems, military air communications, and software-defined networking. E-mail: lvnn2007@163.com||CHEN Kefan was born in 1990. He received his B.S. degree from the University of Electronic Science and Technology of China in 2013, and M.S. and Ph.D. degrees from Air Force Engineering University, Xi’an, China, in 2016 and 2019, respectively. He is currently an engineer of Unit 94860 of the PLA. His research interests include airborne tactical networks, software-defined networking, and aviation data link systems. E-mail: 1148180199@qq.com||CHEN Zhuo was born in 1996. He received his B.S. degree from Yunnan University. He is currently persuing his M.S. degree in Air Force Engineering University, Xi’an, China. His research interests include airborne networks, anomaly detection, and machine learning. E-mail: 408258313@qq.com||GAO Weiting was born in 1984. He received his B.S., M.S., and Ph.D. degrees from Northwestern Polytechnical University in 2007, 2011, and 2015, respectively. He is currently a lecturer in Air Force Engineering University. His research interests include airborne networks and communication system design. E-mail: 519105941@qq.com
Supported by:
This work was supported by the National Natural Science Foundation of China (61701521) and the Shaanxi Provincial Natural Science Foundation (2018JQ6074)

Abstract

Abstract:

The evolution of airborne tactical networks (ATNs) is impeded by the network ossification problem. As a solution, network virtualization (NV) can provide a flexible and scalable architecture where virtual network embedding (VNE) is a key part. However, existing VNE algorithms cannot be optimally adopted in the virtualization of ATN due to the complex interference in air-combat field. In this context, a highly reliable VNE algorithm based on the transmission rate for ATN virtualization (TR-ATVNE) is proposed to adapt well to the specific electromagnetic environment of ATN. Our algorithm coordinates node and link mapping. In the node mapping, transmission-rate resource is firstly defined to effectively evaluate the ranking value of substrate nodes under the interference of both environmental noises and enemy attacks. Meanwhile, a feasible splitting rule is proposed for path splitting in the link mapping, considering the interference between wireless links. Simulation results reveal that our algorithm is able to improve the acceptance ratio of virtual network requests while maintaining a high revenue-to-cost ratio under the complex electromagnetic interference.

Key words: airborne tactical network (ATN), network virtualization (NV), resource allocation, virtual network embedding (VNE), transmission rate

Jingcheng MIAO, Na LYU, Kefan CHEN, Zhuo CHEN, Weiting GAO. A highly reliable embedding algorithm for airborne tactical network virtualization[J]. Journal of Systems Engineering and Electronics, 2021, 32(6): 1364-1374.

Figures/Tables 13

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References 26

1	KOTT A, SWAMI A, WEST B J The Internet of Battle Things. Computer, 2016, 49 (12): 70- 75. doi: 10.1109/MC.2016.355
2	CHEN K F, ZHAO S H, LV N, et al Segment routing based traffic scheduling for the software-defined airborne backbone network. IEEE Access, 2019, 7, 106162- 106178. doi: 10.1109/ACCESS.2019.2930229
3	LYU N, LIU C, CHEN K F, et al A method for centralized control network employment of aeronautic swarm. Acta Aeronautica et Astronautica Sinica, 2018, 39 (7): 321961.
4	LYV N, CAO F B, CHEN K F, et al Adaptive neighbor detection method of software-defined airborne net-work for aeronautic swarm. Systems Engineering and Electronics, 2019, 41 (10): 2260- 2270.
5	LIANG C, YU F R Wireless network virtualization: a survey, some research issues and challenges. IEEE Communications Surveys & Tutorials, 2015, 17 (1): 358- 380.
6	KHAN I, BELQASMI F, GLITHO R, et al Wireless sensor network virtualization: a survey. IEEE Communications Surveys & Tutorials, 2016, 18 (1): 553- 576.
7	VAN DE BELT J, AHMADI H, DOYLE L E Defining and surveying wireless link virtualization and wire-less network virtualization. IEEE Communications Surveys & Tutorials, 2017, 19 (3): 1603- 1627.
8	CAO H T, HU H, QU Z C, et al Heuristic solutions of virtual network embedding: a survey. China Communications, 2018, 15 (3): 186- 219. doi: 10.1109/CC.2018.8332001
9	FISCHER A, BOTERO J F, BECK M T, et al Virtual network embedding: a survey. IEEE Communications surveys & Tutorials, 2013, 15 (4): 1888- 1906.
10	CAO H T, YANG L X, ZHU H B Novel node-ranking approach and multiple topology attributes-based embedding algorithm for single-domain virtual net-work embedding. IEEE Internet of Things Journal, 2018, 5 (1): 108- 120. doi: 10.1109/JIOT.2017.2773489
11	NASHID S, SHIHABUR R C, REAZ A, et al Virtual network survivability through joint space capacity allocation and embedding. IEEE Journal on Selected Areas in Communications, 2018, 36 (3): 502- 518. doi: 10.1109/JSAC.2018.2815430
12	WANG X J, SONG M, YUAN D Y, et al Robust virtual network embedding based on component connectivity in large-scale network. China Communications, 2017, 14 (10): 164- 179. doi: 10.1109/CC.2017.8107641
13	CHOWDHURY M, RAHMAN M R, BOUTABA R ViNEyard:virtual network embedding algorithms with coordinated node and link mapping. IEEE/ACM Trans. on Networking, 2012, 20 (1): 206- 219. doi: 10.1109/TNET.2011.2159308
14	YANG F, QIN S, XU Z Q, et al Dynamic virtual optical network mapping algorithm with cooperation be-tween nodes and links. Journal of Xidian University, 2018, 45 (4): 63- 68.
15	ABDELWAHAB S, HAMDAOUI B, GUIZANI M Efficient virtual network embedding with backtrack avoidance for dynamic wireless networks. IEEE Trans. on Wireless Communications, 2016, 15 (4): 2669- 2683. doi: 10.1109/TWC.2015.2507134
16	YUN D, OK J, SHIN B, et al Embedding of virtual network requests over static wireless multi hop networks. Computer Networks, 2013, 57 (5): 1139- 1152. doi: 10.1016/j.comnet.2012.12.006
17	KAIWARTYA O, ABDULLAH A H, CAO Y, et al Virtualization in wireless sensor networks: fault tolerant embedding for Internet of Things. IEEE Internet of Things Journal, 2018, 5 (2): 571- 580. doi: 10.1109/JIOT.2017.2717704
18	LI M, CHEN C, HUA C, et al Intelligent latency-aware virtual network embedding for industrial wireless networks. IEEE Internet of Things Journal, 2019, 6 (5): 7484- 7496. doi: 10.1109/JIOT.2019.2900855
19	CAO B, XIA S, HE F, et al Research of embedding algorithm for wireless network virtualization. Journal on Communications, 2017, 38 (1): 35- 43.
20	YU M, YI Y, REXFORD J, et al Rethinking virtual network embedding: substrate support for path splitting and migration. SIGCOMM Computer Communication Review, 2008, 38 (2): 17- 29. doi: 10.1145/1355734.1355737
21	HAERI S, DING Q Y, LI Z D, et al Global resource capacity algorithm with path splitting for virtual network embedding. Proc. of the IEEE International Symposium on Circuits and Systems, 2016, 666- 669.
22	ZHAO S H, CHEN K F, LV N, et al A software defined airborne tactical network for aeronautic. Journal on Communications, 2017, 38 (8): 140- 155.
23	BERA S, MISRA S, VASILAKOS A V Software-defined networking for Internet of Things: a survey. IEEE Internet of Things Journal, 2007, 4 (6): 1994- 2008.
24	HAN Y, HYUN J, HONG J W Graph abstraction based virtual network management framework for SDN. Proc. of the IEEE Conference on Computer Communications Workshops, 2016, 884- 885.
25	CORMEN T H, LEISERSON C E, STEIN C, et al. Introduction to algorithms. 2nd ed. Cambridge, USA: McGraw-Hill, 2001.
26	SU Y Z, MENG X R, KANG Q Y, et al Survivable virtual network link protection method based on network coding and protection circuit. IEEE Access, 2018, 6, 67477- 67493. doi: 10.1109/ACCESS.2018.2878797

Description	Mathematical representation
Basic power, available power	$ {p_b}\left( {{n^S}} \right) $ , $ p\left( {{n^S}} \right) $
Node location attribute	${\rm{loc}}\left( { {n^S} } \right)$
Link bandwidth attribute	$ b\left( {{l^S}} \right) $
Link location attribute	${\rm{loc}}\left( { {l^S} } \right)$
All substrate paths set	$ {P^S} $

Description	Mathematical representation
Location	${\rm{loc}}\left( { {n^V} } \right)$
Coverage radius of $ {n^V} $	$ {\varphi ^V} $
Transmission rate	$ R\left( {{l^V}} \right) $

Description	ATSN	ATVN
Number of nodes	50	An integer, uniform distributed [3,5]
Number of links	139	Link connection probability equal to 0.5
Node power	An integer, uniform distributed [5,10]	－
Link bandwidth	An integer, uniform distributed [2,5]	－
Transmission rate	－	An integer, uniform distributed [3,8]
VN arrival rate	－	A Poisson process with an average arrival rate of 5 per 100 time units
Duration time	－	100 time units
Total run time	10 000 time units (100 time windows)

Algorithm	Description
TR-ATVNE	Node mapping evaluates the ranking value by transmission-rate resource while link mapping adopts a feasible minimum-cost path splitting rule.
JBP-WVNE[19]	Node mapping evaluates the embedding value by expanded resources while link mapping uses path splitting while allocating power and bandwidth resources.
B-VNE[26]	Node mapping evaluates the embedding value only by power resource while link mapping finds a single path by the shortest path algorithm.

[1]	Ting CHENG, Xi LI, Qianqian TAN, Yang SU. Adaptive time-space resource and waveform control for collocated MIMO radar with simultaneous multi-beam [J]. Journal of Systems Engineering and Electronics, 2022, 33(1): 47-59.
[2]	Xilin ZHANG, Yuejin TAN, Zhiwei YANG. Resource allocation optimization of equipment development task based on MOPSO algorithm [J]. Journal of Systems Engineering and Electronics, 2019, 30(6): 1132-1143.
[3]	Mengmeng ZHANG, Honghui CHEN, Junxian LIU. Resource allocation approach to associate business-IT alignment to enterprise architecture design [J]. Journal of Systems Engineering and Electronics, 2019, 30(2): 343-351.
[4]	Weixu Dai, Weiwei Wu, Bo Yu, and Yunhao Zhu. Success probability orientated optimization model for resource allocation of the technological innovation multi-project system [J]. Journal of Systems Engineering and Electronics, 2016, 27(6): 1227-1237.
[5]	Su Pan, Cheng Li, Sheng Zhang, and Danwei Chen. Cross-layer resource allocation based on equivalent bandwidth in OFDMA systems [J]. Systems Engineering and Electronics, 2016, 27(4): 754-.
[6]	Xiaolong Xu, Lingling Cao, and Xinheng Wang. Resource pre-allocation algorithms for low-energy task scheduling of cloud computing [J]. Journal of Systems Engineering and Electronics, 2016, 27(2): 457-469.
[7]	Xiong Fu and Yeliang Cang. Task scheduling and virtual machine allocation policy in cloud computing environment [J]. Journal of Systems Engineering and Electronics, 2015, 26(4): 847-.
[8]	Haitao Yuan, Jing Bi, and Bohu Li. Workload-aware request routing in cloud data center using software-defined networking [J]. Journal of Systems Engineering and Electronics, 2015, 26(1): 151-.
[9]	Gongbing Bi, Hailing Wang, and Jingjing Ding. Integration of α-fairness with DEA based resource allocation model [J]. Journal of Systems Engineering and Electronics, 2014, 25(6): 1027-1036.
[10]	Tianran Zhou, Huagang Xiong, and Zhen Zhang. Hierarchical resource allocation for integrated modular avionics systems [J]. Journal of Systems Engineering and Electronics, 2011, 22(5): 780-787.
[11]	Jing Li1, Jianhua Ge, Yong Wang, and Ming Gao. Resource allocation for cooperative diversity systems based on quadrature modulation [J]. Journal of Systems Engineering and Electronics, 2011, 22(2): 327-333.
[12]	Bo Wu*, Jun Shen, and Haige Xiang. Resource allocation with minimum transmit power in multicast OFDM systems [J]. Journal of Systems Engineering and Electronics, 2010, 21(3): 355-360.
[13]	Zhang Chengwen, Liao Minghong, Zhang Zhongzhao & Meng Weixiao. Resource allocation for multiuser STBC-BF MIMO-OFDM downlink with imperfect channel information [J]. Journal of Systems Engineering and Electronics, 2009, 20(4): 687-693.
[14]	Yi Yang, Li Xiaoxing & Gu Chunqin. Hybrid particle swarm optimization for multiobjective resource allocation [J]. Journal of Systems Engineering and Electronics, 2008, 19(5): 959-964.
[15]	Zhang Chengwen, Zhang Zhongzhao & Ma Yongkui. Resource allocation with CCI suppression for multiuser MIMO-OFDM downlink in correlated channels [J]. Journal of Systems Engineering and Electronics, 2008, 19(2): 213-219.

A highly reliable embedding algorithm for airborne tactical network virtualization

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