Modelo Adaptativo para Previsão de Recursos de Rede em Provedores de Internet Modernos
Abstract
Modern Internet Service Providers (MISPs) need to address situations such as the elastic demand for network resources that can affect the Quality of Service (QoS). A promising approach to deal with elastic demand is the usage of a network traffic prediction technique. Within this context, this summary presents an adaptive network prediction model for MISPs that adjusts seasonality and trend and removes time series error cycles according to the behavior observed in network traffic. The results, using a real bandwidth data set, suggest that the proposed model improves the existing prediction models.
References
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.
Published
2021-08-16
How to Cite
OLIVEIRA, Dyego H. L.; GOMES, Rafael L..
Modelo Adaptativo para Previsão de Recursos de Rede em Provedores de Internet Modernos. In: DISSERTATION DIGEST - BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (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.
