Orquestração Inteligente de Network Slicing: Revisão da Literatura e Prospecção para Redes 6G
Resumo
Neste trabalho, investiga-se os desafios de pesquisa relacionados à gerência e orquestração inteligente de network slices nas redes 5G e de próxima geração. Particularmente, a literatura é revisada com o objetivo de compreender os principais problemas abordados neste escopo, bem como as técnicas de Aprendizado de Máquina geralmente empregadas para a solução de tais problemas. Além disso, discute-se questões em aberto e novos desafios que as redes 6G imporão à gerência e orquestração de slices.
Referências
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.
Publicado
16/08/2021
Como Citar
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.
