Orquestração Inteligente de Network Slicing: Revisão da Literatura e Prospecção para Redes 6G
Abstract
In this paper, we investigate research challenges on intelligent management and orchestration of network slices in 5G networks and Beyond. In particular, we review the literature in order to understand the main problems involving this issue, as well as machine learning techniques usually employed to solve such problems. We also discuss open issues and new challenges on management and orchestration of network slices imposed by 6G networks.
References
3GPP (2018). Telecommunication Management; Study on Management and Orchestration of Network Slicing for Next Generation Network.
Bega, D. et al. (2017). Optimising 5G infrastructure markets: The business of network slicing. In IEEE INFOCOM 2017 IEEE Conference on Computer Communications, pages 1–9.
Bega, D. et al. (2019). DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning. In IEEE INFOCOM 2019 IEEE Conference on Computer Communications, pages 280–288.
Benzaid, C. and Taleb, T. (2020). AI-Driven Zero Touch Network and Service Management in 5G and Beyond: Challenges and Research Directions. IEEE Network, 34(2):186–194.
Bin, H. and Schotten, H. D. (2019). Machine Learning for Network Slicing Resource Management: A Comprehensive Survey. ZTE COMMUNICATIONS, 17(4):27–32.
Debbabi, F., Jmal, R., Fourati, L. C., and Ksentini, A. (2020). Algorithmics and Modeling Aspects of Network Slicing in 5G and Beyonds Network: Survey. IEEE Access, 8:162748–162762.
ETSI (2014). Network Functions Virtualisation (NFV) Management and Orchestration.
Gutierrez-Estevez et al. (2019). Artificial Intelligence for Elastic Management and Orchestration of 5G Networks. IEEE Wireless Communications, 26(5):134–141.
Haeri, S. and Trajkovíc, L. (2018). Virtual Network Embedding via Monte Carlo Tree Search. IEEE Transactions on Cybernetics, 48(2):510–521.
Han, B., Feng, D., and Schotten, H. D. (2019). A Markov Model of Slice Admission Control. IEEE Networking Letters, 1(1):2–5.
Li, R. et al. (2018). Deep Reinforcement Learning for Resource Management in Network Slicing. IEEE Access, 6:74429–74441.
NGMN Alliance (2016). Description of Network Slicing Concept.
Quang, P. T. A., Hadjadj-Aoul, Y., and Outtagarts, A. (2019). A Deep Reinforcement IEEE Transactions on Learning Approach for VNF Forwarding Graph Embedding. Network and Service Management, 16(4):1318–1331.
Saad, W., Bennis, M., and Chen, M. (2020). A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems. IEEE Network, 34(3):134– 142.
Zanzi, L. et al. (2021). LACO: A Latency-Driven Network Slicing Orchestration in IEEE Transactions on Wireless Communications, 20(1):667– Beyond-5G Networks. 682.
Bega, D. et al. (2017). Optimising 5G infrastructure markets: The business of network slicing. In IEEE INFOCOM 2017 IEEE Conference on Computer Communications, pages 1–9.
Bega, D. et al. (2019). DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning. In IEEE INFOCOM 2019 IEEE Conference on Computer Communications, pages 280–288.
Benzaid, C. and Taleb, T. (2020). AI-Driven Zero Touch Network and Service Management in 5G and Beyond: Challenges and Research Directions. IEEE Network, 34(2):186–194.
Bin, H. and Schotten, H. D. (2019). Machine Learning for Network Slicing Resource Management: A Comprehensive Survey. ZTE COMMUNICATIONS, 17(4):27–32.
Debbabi, F., Jmal, R., Fourati, L. C., and Ksentini, A. (2020). Algorithmics and Modeling Aspects of Network Slicing in 5G and Beyonds Network: Survey. IEEE Access, 8:162748–162762.
ETSI (2014). Network Functions Virtualisation (NFV) Management and Orchestration.
Gutierrez-Estevez et al. (2019). Artificial Intelligence for Elastic Management and Orchestration of 5G Networks. IEEE Wireless Communications, 26(5):134–141.
Haeri, S. and Trajkovíc, L. (2018). Virtual Network Embedding via Monte Carlo Tree Search. IEEE Transactions on Cybernetics, 48(2):510–521.
Han, B., Feng, D., and Schotten, H. D. (2019). A Markov Model of Slice Admission Control. IEEE Networking Letters, 1(1):2–5.
Li, R. et al. (2018). Deep Reinforcement Learning for Resource Management in Network Slicing. IEEE Access, 6:74429–74441.
NGMN Alliance (2016). Description of Network Slicing Concept.
Quang, P. T. A., Hadjadj-Aoul, Y., and Outtagarts, A. (2019). A Deep Reinforcement IEEE Transactions on Learning Approach for VNF Forwarding Graph Embedding. Network and Service Management, 16(4):1318–1331.
Saad, W., Bennis, M., and Chen, M. (2020). A Vision of 6G Wireless Systems: Applications, Trends, Technologies, and Open Research Problems. IEEE Network, 34(3):134– 142.
Zanzi, L. et al. (2021). LACO: A Latency-Driven Network Slicing Orchestration in IEEE Transactions on Wireless Communications, 20(1):667– Beyond-5G Networks. 682.
Published
2021-08-16
How to Cite
LIMA, Henrique V. de; SILVA, Rogério S.; BOTH, Cristiano B.; OLIVEIRA-JR, Antonio; CARDOSO, Kleber V.; CORRÊA, Sand L..
Orquestração Inteligente de Network Slicing: Revisão da Literatura e Prospecção para Redes 6G. In: WORKSHOP DE REDES 6G (W6G), 1. , 2021, Evento Online.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 19-24.
DOI: https://doi.org/10.5753/w6g.2021.17230.
