Modelo Adaptativo para Previsão de Recursos de Rede em Provedores de Internet Modernos
Resumo
Provedores de Internet Modernos (MISPs) precisam lidar com circunstâncias como a demanda elástica de recursos de rede que podem causar queda na Qualidade de Serviço (QoS). Uma abordagem promissora para lidar com demanda elástica é o uso de técnicas de previsão de tráfego. Neste contexto, este resumo apresenta o modelo adaptável de previsão de rede para MISPs, que ajusta sazonalidade e tendência e remove os ciclos de erro na série temporal de acordo com o comportamento observado no tráfego de rede. Os resultados, usando um conjunto de dados real, sugerem que o modelo proposto aprimora as técnicas de previsão tradicionais.
Referências
Bayati, A., Khoa Nguyen, K., and Cheriet, M. (2018). Multiple-step-ahead traffic prediction in high-speed networks. IEEE Communications Letters, 22(12):2447–2450.
Foukas, X., Patounas, G., Elmokashfi, A., and Marina, M. K. (2017). Network slicing in 5g: Survey and challenges. IEEE Communications Magazine, 55(5):94–100.
Hou, Z., She, C., Li, Y., Quek, T. Q. S., and Vucetic, B. (2018). Burstiness aware In 2018 IEEE Inbandwidth reservation for uplink transmission in tactile internet. ternational Conference on Communications Workshops (ICC Workshops), pages 1–6.
Katris, C. and Daskalaki, S. (2019). Dynamic bandwidth allocation for video traffic using farima-based forecasting models. Journal of Network and Systems Management, 27(1):39–65.
Maleki, A., Nasseri, S., Aminabad, M. S., and Hadi, M. (2018). Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant’s Inuent Characteristics. KSCE Journal of Civil Engineering, 22(9):3233–3245.
Oliveira, D., Filho, F., de Araújo, T., Júnior, J. C., and Gomes, R. (2020). Modelo adaptativo para previsão de recursos de rede em provedores de internet modernos. In Anais do XXV Workshop de Gerência e Operação de Redes e Serviços, pages 209–222, Porto Alegre, RS, Brasil. SBC.
Oliveira, D. H. L., de Araujo, T. P., and Gomes, R. L. (2021). An adaptive forecasting model for slice allocation in softwarized networks. IEEE Transactions on Network and Service Management, 18(1):94–103.
Oliveira, D. H. L., Filho, F. M. V., de Araújo, T. P., Celestino, J., and Gomes, R. L. (2020). Adaptive model for network resources prediction in modern internet service providers. In 2020 IEEE Symposium on Computers and Communications (ISCC), pages 1–6.
Oliveira, D. H. L. and Gomes, R. L. (2020). Bandwidth usage of university campus. DOI: 10.21227/jw40-y336. IEEE Dataport.
Ruan, L., Mondal, S., and Wong, E. (2018). Machine learning based bandwidth prediction in tactile heterogeneous access networks. In IEEE INFOCOM 2018 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 1–2.
Wang, T., Guo, Z., Chen, H., and Liu, W. (2018). Bwmanager: Mitigating denial of service attacks in software-dened networks through bandwidth prediction. IEEE Transactions on Network and Service Management, 15(4):1235–1248.
Foukas, X., Patounas, G., Elmokashfi, A., and Marina, M. K. (2017). Network slicing in 5g: Survey and challenges. IEEE Communications Magazine, 55(5):94–100.
Hou, Z., She, C., Li, Y., Quek, T. Q. S., and Vucetic, B. (2018). Burstiness aware In 2018 IEEE Inbandwidth reservation for uplink transmission in tactile internet. ternational Conference on Communications Workshops (ICC Workshops), pages 1–6.
Katris, C. and Daskalaki, S. (2019). Dynamic bandwidth allocation for video traffic using farima-based forecasting models. Journal of Network and Systems Management, 27(1):39–65.
Maleki, A., Nasseri, S., Aminabad, M. S., and Hadi, M. (2018). Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant’s Inuent Characteristics. KSCE Journal of Civil Engineering, 22(9):3233–3245.
Oliveira, D., Filho, F., de Araújo, T., Júnior, J. C., and Gomes, R. (2020). Modelo adaptativo para previsão de recursos de rede em provedores de internet modernos. In Anais do XXV Workshop de Gerência e Operação de Redes e Serviços, pages 209–222, Porto Alegre, RS, Brasil. SBC.
Oliveira, D. H. L., de Araujo, T. P., and Gomes, R. L. (2021). An adaptive forecasting model for slice allocation in softwarized networks. IEEE Transactions on Network and Service Management, 18(1):94–103.
Oliveira, D. H. L., Filho, F. M. V., de Araújo, T. P., Celestino, J., and Gomes, R. L. (2020). Adaptive model for network resources prediction in modern internet service providers. In 2020 IEEE Symposium on Computers and Communications (ISCC), pages 1–6.
Oliveira, D. H. L. and Gomes, R. L. (2020). Bandwidth usage of university campus. DOI: 10.21227/jw40-y336. IEEE Dataport.
Ruan, L., Mondal, S., and Wong, E. (2018). Machine learning based bandwidth prediction in tactile heterogeneous access networks. In IEEE INFOCOM 2018 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), pages 1–2.
Wang, T., Guo, Z., Chen, H., and Liu, W. (2018). Bwmanager: Mitigating denial of service attacks in software-dened networks through bandwidth prediction. IEEE Transactions on Network and Service Management, 15(4):1235–1248.
Publicado
16/08/2021
Como Citar
OLIVEIRA, Dyego H. L.; GOMES, Rafael L..
Modelo Adaptativo para Previsão de Recursos de Rede em Provedores de Internet Modernos. 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. 161-168.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc_estendido.2021.17167.
