Admission Control and Resource Allocation in 5G Network Slicing
Resumo
Este artigo resume a tese de mestrado intitulada "Admission Control and Resource Allocation in 5G Network Slicing". Propomos duas soluções, SARA e DSARA, que usam algoritmos de Reinforcement Learning para aprender a política de admissão que otimiza o lucro dos provedores. A alocação de recursos considera os requisitos de QoS. Os resultados mostram o bom desempenho de nossas soluções para o Fatiamento de Rede 5G em relação ao lucro e à utilização de recursos.Referências
Agarwal, S., Malandrino, F., Chiasserini, C. F., and De, S. (2019). Vnf placement and IEEE/ACM resource allocation for the support of vertical services in 5g networks. Transactions on Networking, 27(1):433–446.
Bega, D., Gramaglia, M., Banchs, A., Sciancalepore, V., and Costa-Pérez, X. (2019). A machine learning approach to 5g infrastructure market optimization. IEEE Transactions on Mobile Computing, 19(3):498–512.
D’Oro, S., Bonati, L., Restuccia, F., Polese, M., Zorzi, M., and Melodia, T. (2020). Sledge: Network slicing at the edge. arXiv preprint arXiv:2005.00886.
Etsi, N. (2013). Etsi gs nfv 002 v1. 1.1 network functions virtualization (nfv). Architecture and Framework: ONF.
Han, B., Sciancalepore, V., Costa-Perez, X., Feng, D., and Schotten, H. D. (2020). Multiservice-based network slicing orchestration with impatient tenants. IEEE Transactions on Wireless Communications, 19(7):5010–5024.
Sciancalepore, V., Costa, X., and Banchs, A. (2019). Rl-nsb: Reinforcement learningbased 5g network slice broker. IEEE/ACM Trans. on Networking, 27(4):1543–1557.
Spinnewyn, B., Isolani, P. H., Donato, C., Botero, J. F., and Latré, S. (2018). Coordinated service composition and embedding of 5g location-constrained network functions. IEEE TNSM, 15(4):1488–1502.
Villota, W. (2020). Admission control and resource allocation in 5g network slicing.
Zhang, Q., Liu, F., and Zeng, C. (2019). Adaptive interference-aware vnf placement for service-customized 5g network slices. In IEEE INFOCOM, pages 2449–2457. IEEE.
Bega, D., Gramaglia, M., Banchs, A., Sciancalepore, V., and Costa-Pérez, X. (2019). A machine learning approach to 5g infrastructure market optimization. IEEE Transactions on Mobile Computing, 19(3):498–512.
D’Oro, S., Bonati, L., Restuccia, F., Polese, M., Zorzi, M., and Melodia, T. (2020). Sledge: Network slicing at the edge. arXiv preprint arXiv:2005.00886.
Etsi, N. (2013). Etsi gs nfv 002 v1. 1.1 network functions virtualization (nfv). Architecture and Framework: ONF.
Han, B., Sciancalepore, V., Costa-Perez, X., Feng, D., and Schotten, H. D. (2020). Multiservice-based network slicing orchestration with impatient tenants. IEEE Transactions on Wireless Communications, 19(7):5010–5024.
Sciancalepore, V., Costa, X., and Banchs, A. (2019). Rl-nsb: Reinforcement learningbased 5g network slice broker. IEEE/ACM Trans. on Networking, 27(4):1543–1557.
Spinnewyn, B., Isolani, P. H., Donato, C., Botero, J. F., and Latré, S. (2018). Coordinated service composition and embedding of 5g location-constrained network functions. IEEE TNSM, 15(4):1488–1502.
Villota, W. (2020). Admission control and resource allocation in 5g network slicing.
Zhang, Q., Liu, F., and Zeng, C. (2019). Adaptive interference-aware vnf placement for service-customized 5g network slices. In IEEE INFOCOM, pages 2449–2457. IEEE.
Publicado
16/08/2021
Como Citar
JÁCOME, William F. Villota; RENDON, Oscar M. Caicedo; FONSECA, Nelson L. S. da.
Admission Control and Resource Allocation in 5G Network Slicing. In: CONCURSO DE TESES E DISSERTAÇÕES - SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 39. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 89-96.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2021.17158.
