Dynamic SFC Embedding Algorithm Assisted by Federated Learning in Space–Air–Ground-Integrated Network Resource Allocation Scenario | IEEE Journals & Magazine | IEEE Xplore

Dynamic SFC Embedding Algorithm Assisted by Federated Learning in Space–Air–Ground-Integrated Network Resource Allocation Scenario


Abstract:

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart c...Show More

Abstract:

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart cities. The space–air–ground-integrated network (SAGIN) could provide a solution to address this challenge. However, SAGIN is heterogeneous, time-varying, and multidimensional information sources, making it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. This article proposes a service provision method based on service function chaining (SFC) to solve this problem. Network function virtualization (NFV) is essential for efficient resource allocation in SAGIN to meet the resource requirements of user service requests. We propose a federated learning (FL)-based algorithm to solve the embedding problem of SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load to balance resource consumption. Then, an SFC scheduling mechanism is proposed that allows SFC reconfiguration to reduce the service blocking rate. Simulation results show that our proposed FL-VNFE algorithm is more advantageous compared to other algorithms, with 12.9%, 2.52%, and 10.5% improvement in long-term average revenue, acceptance rate, and long-term average revenue–cost ratio, respectively.
Published in: IEEE Internet of Things Journal ( Volume: 10, Issue: 11, 01 June 2023)
Page(s): 9308 - 9318
Date of Publication: 15 November 2022

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I. Introduction

Social and businesses need to constantly drive the rapid development of 6G. 6G will provide services in more complex and diverse application scenarios, involving politics, economics, education, healthcare, and many other aspects [1]. The future network communication system will be characterized by all things connected together to achieve ubiquitous connectivity on a global scale [2], [3]. At the same time, through collaborative transmission, the resources of multiple systems are managed uniformly to improve the utilization efficiency of the overall resources. However, existing ground networks operate independently, lack cooperation mechanisms, and have limited coverage. Therefore, the 6G network needs to break through the limitations of the terrain environment and build a space–air–ground-integrated network (SAGIN) across space, air, and ground [4], [5].

Select All
1.
W. Saad, M. Bennis and M. Chen, "A vision of 6G wireless systems: Applications trends technologies and open research problems", IEEE Netw., vol. 34, no. 3, pp. 134-142, May/Jun. 2020.
2.
H. Cui et al., "Space–air–ground integrated network (SAGIN) for 6G: Requirements architecture and challenges", China Commun., vol. 19, no. 2, pp. 90-108, Feb. 2022.
3.
C. Zhang, W. Chen, Q. Chen and C. He, "Distributed intelligent reflecting surfaces-aided device-to-device communications system", J. Commun. Inf. Netw., vol. 6, no. 3, pp. 197-207, Sep. 2021.
4.
P. Zhang, C. Wang, N. Kumar and L. Liu, "Space–air–ground integrated multi-domain network resource orchestration based on virtual network architecture: A DRL method", IEEE Trans. Intell. Transp. Syst., vol. 23, no. 3, pp. 2798-2808, Mar. 2022.
5.
S. Yu, X. Gong, Q. Shi, X. Wang and X. Chen, "EC-SAGINs: Edge-computing-enhanced space–air–ground-integrated networks for Internet of Vehicles", IEEE Internet Things J., vol. 9, no. 8, pp. 5742-5754, Apr. 2022.
6.
X. Wang, L. T. Yang, D. Meng, M. Dong, K. Ota and H. Wang, "Multi-UAV cooperative Localization for marine targets based on weighted subspace fitting in SAGIN environment", IEEE Internet Things J., vol. 9, no. 8, pp. 5708-5718, Apr. 2022.
7.
C. Zhou et al., "Deep reinforcement learning for delay-oriented IoT task scheduling in SAGIN", IEEE Trans. Wireless Commun., vol. 20, no. 2, pp. 911-925, Feb. 2021.
8.
G. Kibalya, J. Serrat, J.-L. Gorricho, D. Okello and P. Zhang, "A deep reinforcement learning-based algorithm for reliability-aware multi-domain service deployment in smart ecosystems", Neural Comput. Appl..
9.
G. Sun, Y. Li, Y. Li, D. Liao and V. Chang, "Low-latency orchestration for workflow-oriented service function chain in edge computing", Future Gener. Comput. Syst., vol. 85, pp. 116-128, Aug. 2018.
10.
N. Toumi, O. Bernier, D.-E. Meddour and A. Ksentini, "On cross-domain service function chain orchestration: An architectural framework", Comput. Netw., vol. 187, Mar. 2021.
11.
Y. Liu, Y. Lu, X. Li, Z. Yao and D. Zhao, "On dynamic service function chain reconfiguration in IoT networks", IEEE Internet Things J., vol. 7, no. 11, pp. 10969-10984, Nov. 2020.
12.
G. Sun, Y. Li, D. Liao and V. Chang, "Service function chain orchestration across multiple domains: A full mesh aggregation approach", IEEE Trans. Netw. Service Manag., vol. 15, no. 3, pp. 1175-1191, Sep. 2018.
13.
D. Zhao, G. Sun, D. Liao, S. Xu and V. Chang, "Mobile-aware service function chain migration in cloud–fog computing", Future Gener. Comput. Syst., vol. 96, pp. 591-604, Jul. 2019.
14.
B. Cao et al., "Edge–cloud resource scheduling in space–air–ground-integrated networks for Internet of Vehicles", IEEE Internet Things J., vol. 9, no. 8, pp. 5765-5772, Apr. 2022.
15.
Y. Xiao et al., "NFVdeep: Adaptive online service function chain deployment with deep reinforcement learning", Proc. IEEE/ACM 27th Int. Symp. Qual. Serv. (IWQoS), pp. 1-10, 2019.
16.
X. Mo and J. Xu, "Energy-efficient federated edge learning with joint communication and computation design", J. Commun. Inf. Netw., vol. 6, no. 2, pp. 110-124, Jun. 2021.
17.
P. Zhang, C. Wang, C. Jiang and Z. Han, "Deep reinforcement learning assisted federated learning algorithm for data management of IIoT", IEEE Trans. Ind. Informat., vol. 17, no. 12, pp. 8475-8484, Dec. 2021.
18.
H. Huang et al., "Scalable orchestration of service function chains in NFV-enabled networks: A federated reinforcement learning approach", IEEE J. Sel. Areas Commun., vol. 39, no. 8, pp. 2558-2571, Aug. 2021.
19.
G. Sun, Z. Xu, H. Yu, X. Chen, V. Chang and A. V. Vasilakos, "Low-latency and resource-efficient service function chaining orchestration in network function virtualization", IEEE Internet Things J., vol. 7, no. 7, pp. 5760-5772, Jul. 2020.
20.
M. Wang, B. Cheng, S. Wang and J. Chen, "Availability- and traffic-aware placement of parallelized SFC in data center networks", IEEE Trans. Netw. Service Manag., vol. 18, no. 1, pp. 182-194, Mar. 2021.
21.
D. Li, P. Hong, K. Xue and J. Pei, "Virtual network function placement considering resource optimization and SFC requests in cloud datacenter", IEEE Trans. Parallel Distrib. Syst., vol. 29, no. 7, pp. 1664-1677, Jul. 2018.
22.
Y. Yue, B. Cheng, X. Liu, M. Wang, B. Li and J. Chen, "Resource optimization and delay guarantee virtual network function placement for mapping SFC requests in cloud networks", IEEE Trans. Netw. Service Manag., vol. 18, no. 2, pp. 1508-1523, Jun. 2021.
23.
Y. Liu, Y. Lu, X. Li, W. Qiao, Z. Li and D. Zhao, "SFC embedding meets machine learning: Deep reinforcement learning approaches", IEEE Commun. Lett., vol. 25, no. 6, pp. 1926-1930, Jun. 2021.
24.
H. A. Alameddine, S. Sebbah and C. Assi, "On the interplay between network function mapping and scheduling in VNF-based networks: A column generation approach", IEEE Trans. Netw. Service Manag., vol. 14, no. 4, pp. 860-874, Dec. 2017.
25.
Y. Liu, H. Lu, X. Li and D. Zhao, "An approach for service function chain reconfiguration in network function virtualization architectures", IEEE Access, vol. 7, pp. 147224-147237, 2019.
26.
T. Gao et al., "Cost-efficient VNF placement and scheduling in public cloud networks", IEEE Trans. Commun., vol. 68, no. 8, pp. 4946-4959, Aug. 2020.
27.
J. Li, W. Shi, N. Zhang and X. S. Shen, "Reinforcement learning based VNF scheduling with end-to-end delay guarantee", Proc. IEEE/CIC Int. Conf. Commun. China (ICCC), pp. 572-577, Aug. 2019.
28.
J. Li, W. Shi, N. Zhang and X. Shen, "Delay-aware VNF scheduling: A reinforcement learning approach with variable action set", IEEE Trans. Cogn. Commun. Netw., vol. 7, no. 1, pp. 304-318, Mar. 2021.
29.
J. Zhang, Y. Tang, T. Ye and Y. Sun, "SFC-based service provisioning for 6G satellite-ground integrated networks", Proc. 10th IEEE/CIC Int. Conf. Commun. China (ICCC), pp. 951-956, Jul. 2021.
30.
J. Li, W. Shi, H. Wu, S. Zhang and X. Shen, "Cost-aware dynamic SFC mapping and scheduling in SDN/NFV-enabled space–air–ground-integrated networks for Internet of Vehicles", IEEE Internet Things J., vol. 9, no. 8, pp. 5824-5838, Apr. 2022.

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References is not available for this document.