Dynamic Network Slice Migration Algorithm Based on Ensemble Deep Neural Network Traffic Prediction

TANG Lun; ZHOU Xinlong; WU Ting; WANG Kai; CHEN Qianbin

doi:10.11999/JEIT220058

Volume 45 Issue 3

Mar. 2023

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Article Navigation > Journal of Electronics & Information Technology > 2023 > 45(3): 1074-1082

Wen Xinhui, Niu Mingji . THE ABS LEARNING ALGORITHM FOR RADIAL BASIS FUNCTIONAL NETWORK[J]. Journal of Electronics & Information Technology, 1996, 18(6): 601-606.

Citation:

TANG Lun, ZHOU Xinlong, WU Ting, WANG Kai, CHEN Qianbin. Dynamic Network Slice Migration Algorithm Based on Ensemble Deep Neural Network Traffic Prediction[J]. Journal of Electronics & Information Technology, 2023, 45(3): 1074-1082. doi: 10.11999/JEIT220058

Wen Xinhui, Niu Mingji . THE ABS LEARNING ALGORITHM FOR RADIAL BASIS FUNCTIONAL NETWORK[J]. Journal of Electronics & Information Technology, 1996, 18(6): 601-606.

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Dynamic Network Slice Migration Algorithm Based on Ensemble Deep Neural Network Traffic Prediction

doi: 10.11999/JEIT220058

1.
School of Communication and Information Engineering, Chongqing University of Post and Telecommunications, Chongqing 400065, China
2.
Key Laboratory of Mobile Communication Technology, Chongqing University of Post and Telecommunications, Chongqing 400065, China

Funds: The National Natural Science Foundation of China (62071078), The Science and Technology Research Program of Chongqing Municipal Education Commission (KJZD-M201800601), Sichuan and Chongqing Key R&D Projects (2021YFQ0053)

Received Date: 2022-01-13
Rev Recd Date: 2022-05-23

Available Online: 2022-06-02

Publish Date: 2023-03-10

Abstract

Abstract

In order to solve the problem that slice migration lags behind by lacking awareness of physical network resources in 5G Network Slice (NS) scenarios, a Dynamic Slice Adjustment and Migration (DSAM) algorithm based on ensemble deep neural network traffic prediction is proposed. Firstly, a network total penalty model based on computing, memory and bandwidth resource allocation is established. Secondly, in order to predict the future traffic situation, a prediction algorithm based on ensemble deep neural network is proposed. Then the result of prediction are converted to perception of the physical network resource usage and resource requirements of slice in future according to the different types of traffic. Finally, in order to as large as possible to reduce operators punishment according to the result of perception, Virtual Network Functions (VNF) and virtual links are migrated to physical nodes and links that meet resource limits through dynamic slice adjustment and migration policies. The simulation results show that the proposed algorithm improves effectively the efficiency of slice migration and utilization of network resources.
- Flow prediction,
- Integrated learning,
- Slice migration and adjustment,
- Resources allocation

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References(15)

References

[1]	GHONGE M M, MANGRULKAR R, PJAWANDHIYA P M, et al. Future Trends in 5G and 6G: Challenges, Architecture, and Applications[M]. CRC Press, 2021.
[2]	DEBBABI F, JMAL R, FOURATI L C, et al. Algorithmics and modeling aspects of network slicing in 5G and Beyonds network: Survey[J]. IEEE Access, 2020, 8: 162748–162762. doi: 10.1109/ACCESS.2020.3022162
[3]	POZZA M, NICHOLSON P K, LUGONES D F, et al. On reconfiguring 5G network slices[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(7): 1542–1554. doi: 10.1109/JSAC.2020.2986898
[4]	LI D F, PHONG P L, XUE K P, et al. Virtual network function placement considering resource optimization and SFC requests in cloud datacenter[J]. IEEE Transactions on Parallel and Distributed Systems, 2018, 29(7): 1664–1677. doi: 10.1109/TPDS.2018.2802518
[5]	SCIANCALEPORE V, COSTA-PEREZ X, and BANCHS A. RL-NSB: Reinforcement learning-based 5G network slice broker[J]. IEEE/ACM Transactions on Networking, 2019, 27(4): 1543–1557. doi: 10.1109/TNET.2019.2924471
[6]	XIAO Suchao and CHEN Wen. Dynamic allocation of 5G transport network slice bandwidth based on LSTM traffic prediction[C]. 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 2018: 735–739.
[7]	SONG Chuang, ZHANG Min, HUANG Xuetian, et al. Machine learning enabling traffic-aware dynamic slicing for 5G optical transport networks[C]. 2018 Conference on Lasers and Electro-Optics (CLEO), San Jose, USA, 2018: 1–2.
[8]	唐伦, 赵培培, 赵国繁, 等. 基于深度信念网络资源需求预测的虚拟网络功能动态迁移算法[J]. 电子与信息学报, 2019, 41(6): 1397–1404. doi: 10.11999/JEIT180666 TANG Lun, ZHAO Peipei, ZHAO Guofan, et al. Virtual network function migration algorithm based on deep belief network prediction of resource requirements[J]. Journal of Electronics &Information Technology, 2019, 41(6): 1397–1404. doi: 10.11999/JEIT180666
[9]	LI Taihui, ZHU Xiaorong, and LIU Xu. An end-to-end network slicing algorithm based on deep Q-learning for 5G network[J]. IEEE Access, 2020, 8: 122229–122240. doi: 10.1109/ACCESS.2020.3006502
[10]	WEI Fengsheng, FENG Gang, SUN Yao, et al. Proactive network slice reconfiguration by exploiting prediction interval and robust optimization[C]. GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, China, 2020: 1–6.
[11]	CHERGUI H and VERIKOUKIS C. Offline SLA-constrained deep learning for 5G networks reliable and dynamic end-to-end slicing[J]. IEEE Journal on Selected Areas in Communications, 2020, 38(2): 350–360. doi: 10.1109/JSAC.2019.2959186
[12]	KAO C C, CHANG C W, CHO C P, et al. Deep learning and ensemble learning for traffic load prediction in real network[C]. 2020 IEEE Eurasia Conference on IOT, Communication and Engineering (ECICE), Yunlin, China, 2020: 36–39.
[13]	YIN Shan, ZHANG Zhan, YANG Chen, et al. Prediction-based end-to-end dynamic network slicing in hybrid elastic fiber-wireless networks[J]. Journal of Lightwave Technology, 2021, 39(7): 1889–1899. doi: 10.1109/JLT.2020.3045600
[14]	YU Hao, MUSUMECI F, ZHANG Jiawei, et al. Dynamic 5G RAN slice adjustment and migration based on traffic prediction in WDM metro-aggregation networks[J]. Journal of Optical Communications and Networking, 2020, 12(12): 403–413. doi: 10.1364/JOCN.403829
[15]	Small Cell Forum. Small cell virtualization: Functional splits and use cases[R]. Technical Report of Small Cell Forum, 2016.

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