Um framework NWDAF para algoritmos de análise de dados de rede 5G e além
Resumo
A função de análise de dados de redes 5G, definida pela 3GPP, é responsável por analisar os dados de rede e propiciar a execução de ações na rede como resultado da análise. Os sistema de análise de dados de rede 5G da literatura utilizam algoritmos para um único contexto de análise e não permitem a inclusão de novos algoritmos de análise. Como alternativa, este trabalho apresenta um framework para a função de análise dados de redes 5G que permite a inclusão e edição de algoritmos de análise de dados, em uma arquitetura composta por software open-source. Como um estudo de caso, técnicas de inteligência artificial no contexto de detecção de anomalias de redes são incorporadas ao framework.Referências
3rd Generation Partnership Project (3GPP) TS 29.520. ”5g system; network data analytics services, version 18.3.0 release 18”. [link]. Acesso em outubro de 2023.
Abbas, K., Khan, T. A., Afaq, M., Diaz Rivera, J. J., and Song, W.-C. (2021). Network data analytics function for ibn-based network slice lifecycle management. In 2021 22nd Asia-Pacific Network Operations and Management Symposium (APNOMS), pages 148–153.
Aumayr, E., Caso, G., Bosneag, A.-M., Zayas, A. D., Özgü Alay, Garcia, B., Kousias, K., Brünstrom, A., Gomez, P. M., and Koumaras, H. (2022). Service-based analytics for 5g open experimentation platforms. Computer Networks, 205:108740.
Coldwell, C., Conger, D., Goodell, E., Jacobson, B., Petersen, B., Spencer, D., Anderson, M., and Sgambati, M. (2022). Machine learning 5g attack detection in programmable logic. In 2022 IEEE Globecom Workshops (GC Wkshps), pages 1365–1370.
Donertasli, H. and Medithe, M. (2022). Nwdaf udi (use-case development interface) for end-to-end ai enabled 5g and beyond networks. In 2022 International Conference on Artificial Intelligence of Things (ICAIoT), pages 1–6.
Ma, C. and Chi, Y. (2022). Knn normalized optimization and platform tuning based on hadoop. IEEE Access, 10:81406–81433.
Oliveira, J. M., Almeida, J., Macedo, D., and Nogueira, J. M. (2023). Comparative analysis of unsupervised machine learning algorithms for anomaly detection in network data. In 2023 IEEE Latin-American Conference on Communications (LATINCOM), pages 1–6.
Perez, R., Garcia-Reinoso, J., Zabala, A., Serrano, P., and Banchs, A. (2020). A monitoring framework for multi-site 5g platforms. In 2020 European Conference on Networks and Communications (EuCNC), pages 52–56.
Samaras, G., Theodorou, V., Laskaratos, D., Psaromanolakis, N., Mertiri, M., and Valantasis, A. (2022). Qmp: A cloud-native mlops automation platform for zero-touch service assurance in 5g systems. In 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), pages 86–89.
Tavares, I., Oliveira, J. M., Teixeira, A. F., de Arruda Pereira, M., Kakitani, M. T., and Nogueira, J. M. (2022). Machine learning algorithms applied to telemetry data of scd-2 brazilian satellite. In Proceedings of the 2022 Latin America Networking Conference, LANC ’22, page 50–58, New York, NY, USA. Association for Computing Machinery.
Abbas, K., Khan, T. A., Afaq, M., Diaz Rivera, J. J., and Song, W.-C. (2021). Network data analytics function for ibn-based network slice lifecycle management. In 2021 22nd Asia-Pacific Network Operations and Management Symposium (APNOMS), pages 148–153.
Aumayr, E., Caso, G., Bosneag, A.-M., Zayas, A. D., Özgü Alay, Garcia, B., Kousias, K., Brünstrom, A., Gomez, P. M., and Koumaras, H. (2022). Service-based analytics for 5g open experimentation platforms. Computer Networks, 205:108740.
Coldwell, C., Conger, D., Goodell, E., Jacobson, B., Petersen, B., Spencer, D., Anderson, M., and Sgambati, M. (2022). Machine learning 5g attack detection in programmable logic. In 2022 IEEE Globecom Workshops (GC Wkshps), pages 1365–1370.
Donertasli, H. and Medithe, M. (2022). Nwdaf udi (use-case development interface) for end-to-end ai enabled 5g and beyond networks. In 2022 International Conference on Artificial Intelligence of Things (ICAIoT), pages 1–6.
Ma, C. and Chi, Y. (2022). Knn normalized optimization and platform tuning based on hadoop. IEEE Access, 10:81406–81433.
Oliveira, J. M., Almeida, J., Macedo, D., and Nogueira, J. M. (2023). Comparative analysis of unsupervised machine learning algorithms for anomaly detection in network data. In 2023 IEEE Latin-American Conference on Communications (LATINCOM), pages 1–6.
Perez, R., Garcia-Reinoso, J., Zabala, A., Serrano, P., and Banchs, A. (2020). A monitoring framework for multi-site 5g platforms. In 2020 European Conference on Networks and Communications (EuCNC), pages 52–56.
Samaras, G., Theodorou, V., Laskaratos, D., Psaromanolakis, N., Mertiri, M., and Valantasis, A. (2022). Qmp: A cloud-native mlops automation platform for zero-touch service assurance in 5g systems. In 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom), pages 86–89.
Tavares, I., Oliveira, J. M., Teixeira, A. F., de Arruda Pereira, M., Kakitani, M. T., and Nogueira, J. M. (2022). Machine learning algorithms applied to telemetry data of scd-2 brazilian satellite. In Proceedings of the 2022 Latin America Networking Conference, LANC ’22, page 50–58, New York, NY, USA. Association for Computing Machinery.
Publicado
20/05/2024
Como Citar
OLIVEIRA, Júnia Maísa; ALMEIDA, Jônatan; MACEDO, Daniel Fernandes; NOGUEIRA, José Marcos.
Um framework NWDAF para algoritmos de análise de dados de rede 5G e além. In: WORKSHOP DE REDES 6G (W6G), 4. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 9-14.
DOI: https://doi.org/10.5753/w6g.2024.3326.
