A Virtual Network Function Migration Algorithm Based on Federated Learning Prediction of Resource Requirements

TANG Lun; WU Ting; ZHOU Xinlong; CHEN Qianbin

doi:10.11999/JEIT210743

Volume 44 Issue 10

Oct. 2022

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TANG Lun, WU Ting, ZHOU Xinlong, CHEN Qianbin. A Virtual Network Function Migration Algorithm Based on Federated Learning Prediction of Resource Requirements[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3532-3540. doi: 10.11999/JEIT210743

Citation:

TANG Lun, WU Ting, ZHOU Xinlong, CHEN Qianbin. A Virtual Network Function Migration Algorithm Based on Federated Learning Prediction of Resource Requirements[J]. Journal of Electronics & Information Technology, 2022, 44(10): 3532-3540. doi: 10.11999/JEIT210743

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A Virtual Network Function Migration Algorithm Based on Federated Learning Prediction of Resource Requirements

doi: 10.11999/JEIT210743

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

Received Date: 2021-07-27
Rev Recd Date: 2022-03-23

Available Online: 2022-03-30

Publish Date: 2022-10-19

Abstract

Abstract

In order to solve the problem of virtual network function migration caused by time-varying network traffic in network slicing, a Virtual Network Function (VNF) migration algorithm based on Federated learning with Bidirectional Gate Recurrent Units (FedBi-GRU) prediction of resource requirements is proposed. Firstly, a VNF migration model of system energy consumption and load balancing is established, and then a framework based on distributed federated learning is introduced to cooperatively train the predictive model. Secondly, considering predicting the resource requirements of VNF, an online training Bidirectional Gate Recurrent Unit (Bi-GRU) algorithm on the basis of the framework is designed. Finally, on the grounds of the resource prediction results, system energy consumption optimization and load balancing are combined, and a Distributed Proximal Policy Optimization (DPPO) migration algorithm is proposed to formulate a VNF migration strategy in advance. The simulation results show that the combination of the two algorithms reduces effectively the energy consumption of the network system and ensures the load balance.
- Virtual Network Function(VNF),
- Prediction,
- Migration,
- Deep reinforcement learning

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References(19)

References

[1]	LI Defang, HONG Peilin, XUE Kaiping, et al. Availability aware VNF deployment in datacenter through shared redundancy and multi-tenancy[J]. IEEE Transactions on Network and Service Management, 2019, 16(4): 1651–1664. doi: 10.1109/TNSM.2019.2936505
[2]	QU Kaige, ZHUANG Weihua, YE Qiang, et al. Dynamic flow migration for embedded services in SDN/NFV-enabled 5G core networks[J]. IEEE Transactions on Communications, 2020, 68(4): 2394–2408. doi: 10.1109/TCOMM.2020.2968907
[3]	LIU Yicen, LU Hao, LI Xi, et al. An approach for service function chain reconfiguration in network function virtualization architectures[J]. IEEE Access, 2019, 7: 147224–147237. doi: 10.1109/ACCESS.2019.2946648
[4]	TANG Lun, HE Xiaoyu, ZHAO Peipei, et al. Virtual network function migration based on dynamic resource requirements prediction[J]. IEEE Access, 2019, 7: 112348–112362. doi: 10.1109/ACCESS.2019.2935014
[5]	LIU Yicen, LU Yu, LI Xi, et al. On dynamic service function chain reconfiguration in IoT networks[J]. IEEE Internet of Things Journal, 2020, 7(11): 10969–10984. doi: 10.1109/JIOT.2020.2991753
[6]	HUANG Yuzhe, XU Huahu, GAO Honghao, et al. SSUR: An approach to optimizing virtual machine allocation strategy based on user requirements for cloud data center[J]. IEEE Transactions on Green Communications and Networking, 2021, 5(2): 670–681. doi: 10.1109/TGCN.2021.3067374
[7]	DAYARATHNA M, WEN Yonggang, and FAN Rui. Data center energy consumption modeling: A survey[J]. IEEE Communications Surveys & Tutorials, 2015, 18(1): 732–794. doi: 10.1109/COMST.2015.2481183
[8]	ERAMO V, AMMAR M, and LAVACCA F G. Migration energy aware reconfigurations of virtual network function instances in NFV architectures[J]. IEEE Access, 2017, 5: 4927–4938. doi: 10.1109/ACCESS.2017.2685437
[9]	HAN Zhenhua, TAN Haisheng, WANG Rui, et al. Energy-efficient dynamic virtual machine management in data centers[J]. IEEE/ACM Transactions on Networking, 2019, 27(1): 344–360. doi: 10.1109/TNET.2019.2891787
[10]	ZHANG Zhongbao, CAO Huafeng, SU Sen, et al. Energy aware virtual network migration[J]. IEEE Transactions on Cloud Computing, 2022, 10(2): 1173–1189. doi: 10.1109/TCC.2020.2976966.
[11]	GUO Zehua, XU Yang, LIU Yafeng, et al. AggreFlow: Achieving power efficiency, load balancing, and quality of service in data center networks[J]. IEEE/ACM Transactions on Networking, 2020, 29(1): 17–33. doi: 10.1109/TNET.2020.3026015
[12]	LI Biyi, CHENG Bo, LIU Xuan, et al. Joint resource optimization and delay-aware virtual network function migration in data center networks[J]. IEEE Transactions on Network and Service Management, 2021, 18(3): 2960–2974. doi: 10.1109/TNSM.2021.3067883
[13]	ZHANG Kunpeng, WU Lan, ZHU Zhaoju, et al. A multitask learning model for traffic flow and speed forecasting[J]. IEEE Access, 2020, 8: 80707–80715. doi: 10.1109/ACCESS.2020.2990958
[14]	LIU Yi, JAMES J J Q, KANG Jiawen, et al. Privacy-preserving traffic flow prediction: A federated learning approach[J]. IEEE Internet of Things Journal, 2020, 7(8): 7751–7763. doi: 10.1109/JIOT.2020.2991401
[15]	ZHANG Zhenyu, LUO Xiangfeng, LIU Tong, et al. Proximal policy optimization with mixed distributed training[C]. The 2019 IEEE 31st International Conference on Tools with Artificial Intelligence (ICTAI), Portland, USA, 2019: 1452–1456.
[16]	SCHULMAN J, WOLSKI F, DHARIWAL P, et al. Proximal policy optimization algorithms[J]. arXiv preprint arXiv: 1707.06347, 2017.
[17]	BEN YAHIA I G, BENDRISS J, SAMBA A, et al. CogNitive 5G networks: Comprehensive operator use cases with machine learning for management operations[C]. 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN), Paris, France, 2017: 252–259.
[18]	BENDRISS J, BEN YAHIA I G, and ZEGHLACHE D. Forecasting and anticipating SLO breaches in programmable networks[C]. 2017 20th Conference on Innovations in Clouds, Internet and Networks (ICIN), Paris, France, 2017: 127–134.
[19]	BENDRISS J. Cognitive management of SLA in software-based networks[D]. [Ph. D. dissertation], Institut National des Télécommunications, 2018.