Aplicando Redes Neurais e Análise Temporal para Predição Adaptativa de Desempenho de Rede
Resumo
Serviços de monitoramento de rede são executados por diversas empresas e Provedores de Internet (ISPs), que fornecem resultados de testes regulares de desempenho, tais como vazão, perda, atraso, dentre outros. Estas medições auxiliam a conhecer o comportamento da rede, bem como obter informações para um planejamento estratégico. Contudo, estas ferramentas ainda precisam evoluir a fim de englobar atividades mais complexas, tal como predição do desempenho, principalmente dentro do contexto atual de demanda elástica. Dentro deste contexto, este artigo apresenta um modelo adaptativo de predição de desempenho de rede baseado em Redes Neurais e Análise de Séries Temporais, habilitando a identificação do desempenho futuro da rede em determinados períodos, de acordo com medições de rede passadas. Os experimentos realizados, usando dados reais da Rede Nacional de Ensino e Pesquisa (RNP), mostram que o modelo proposto consegue atingir altos níveis de acurácia na predição, bem como supera o uso dos modelos de predição existentes.
Referências
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Blu, T., Thévenaz, P., and Unser, M. (2004). Linear interpolation revitalized. IEEE Transactions on Image Processing, 13(5):710-719.
Cardwell, N., Cheng, Y., Gunn, C. S., Yeganeh, S. H., and Jacobson, V. (2017). Bbr: congestion-based congestion control. Communications of the ACM, 60(2):58-66.
Cleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I. (1990). Stl: A seasonal-trend decomposition. J. Off. Stat, 6(1):3-73.
Costarelli, D., Seracini, M., and Vinti, G. (2020). A comparison between the sampling kantorovich algorithm for digital image processing with some interpolation and quasi-interpolation methods. Applied Mathematics and Computation, 374:125046.
Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. M. (2020). Reliability-aware network slicing in elastic demand scenarios. IEEE Communications Magazine, 58(10):29-34.
Ha, S., Rhee, I., and Xu, L. (2008). Cubic: a new tcp-friendly high-speed tcp variant. ACM SIGOPS operating systems review, 42(5):64-74.
Hewamalage, H., Bergmeir, C., and Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1):388-427.
Kanaya, T., Tabata, N., and Yamaguchi, S. (2020). A study on performance of cubic tcp and tcp bbr in 5g environment. In 2020 IEEE 3rd 5G World Forum (5GWF), pages 508-513.
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.
Lara-Benítez, P., Carranza-García, M., and Riquelme, J. C. (2021). An experimental review on deep learning architectures for time series forecasting. International Journal of Neural Systems, 31(03):2130001. PMID: 33588711.
Maleki, A., Nasseri, S., Aminabad, M. S., and Hadi, M. (2018). Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant's Influent Characteristics. KSCE Journal of Civil Engineering, 22(9):3233-3245.
Menezes, R., Oliveira, D., and Gomes, R. (2021). O impacto da pandemia do covid-19 no comportamento do tráfego de rede e no processo de predição. In Anais do XII Workshop de Pesquisa Experimental da Internet do Futuro, pages 25-30, Porto Alegre, RS, Brasil. SBC.
Miyazawa, K., Yamaguchi, S., and Kobayashi, A. (2020). Mechanism of cyclic performance fluctuation of tcp bbr and cubic tcp communications. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), pages 1139-1144.
Mok, R. K. P., Zou, H., Yang, R., Koch, T., Katz-Bassett, E., and Claffy, K. C. (2021). Measuring the network performance of google cloud platform. In Proceedings of the 21st ACM Internet Measurement Conference, IMC '21, page 54-61, New York, NY, USA. Association for Computing Machinery.
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.
Rafi, S. H., Nahid-Al-Masood, Deeba, S. R., and Hossain, E. (2021). A short-term load forecasting method using integrated cnn and lstm network. IEEE Access, 9:32436-32448.
Sone, S. P., Lehtomäki, J. J., and Khan, Z. (2020). Wireless traffic usage forecasting using real enterprise network data: Analysis and methods. IEEE Open Journal of the Communications Society, 1:777-797.
Syu, Y., Wang, C., and Fanjiang, Y. (2019). Modeling and forecasting of time-aware dynamic qos attributes for cloud services. IEEE Transactions on Network and Service Management, 16(1):56-71.
Tomic, I., Bleakley, E., and Ivanis, P. (2022). Predictive capacity planning for mobile networksmdash;ml supported prediction of network performance and user experience evolution. Electronics, 11(4).
Yang, H., Li, X., Qiang, W., Zhao, Y., Zhang, W., and Tang, C. (2021). A network traffic forecasting method based on sa optimized arima-bp neural network. Computer Networks, 193:108102.
Bayne, L., Schepis, D., and Purchase, S. (2017). A framework for understanding strategic network performance: Exploring efficiency and effectiveness at the network level. Industrial Marketing Management, 67:134-147.
Blu, T., Thévenaz, P., and Unser, M. (2004). Linear interpolation revitalized. IEEE Transactions on Image Processing, 13(5):710-719.
Cardwell, N., Cheng, Y., Gunn, C. S., Yeganeh, S. H., and Jacobson, V. (2017). Bbr: congestion-based congestion control. Communications of the ACM, 60(2):58-66.
Cleveland, R. B., Cleveland, W. S., McRae, J. E., and Terpenning, I. (1990). Stl: A seasonal-trend decomposition. J. Off. Stat, 6(1):3-73.
Costarelli, D., Seracini, M., and Vinti, G. (2020). A comparison between the sampling kantorovich algorithm for digital image processing with some interpolation and quasi-interpolation methods. Applied Mathematics and Computation, 374:125046.
Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. M. (2020). Reliability-aware network slicing in elastic demand scenarios. IEEE Communications Magazine, 58(10):29-34.
Ha, S., Rhee, I., and Xu, L. (2008). Cubic: a new tcp-friendly high-speed tcp variant. ACM SIGOPS operating systems review, 42(5):64-74.
Hewamalage, H., Bergmeir, C., and Bandara, K. (2021). Recurrent neural networks for time series forecasting: Current status and future directions. International Journal of Forecasting, 37(1):388-427.
Kanaya, T., Tabata, N., and Yamaguchi, S. (2020). A study on performance of cubic tcp and tcp bbr in 5g environment. In 2020 IEEE 3rd 5G World Forum (5GWF), pages 508-513.
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.
Lara-Benítez, P., Carranza-García, M., and Riquelme, J. C. (2021). An experimental review on deep learning architectures for time series forecasting. International Journal of Neural Systems, 31(03):2130001. PMID: 33588711.
Maleki, A., Nasseri, S., Aminabad, M. S., and Hadi, M. (2018). Comparison of ARIMA and NNAR Models for Forecasting Water Treatment Plant's Influent Characteristics. KSCE Journal of Civil Engineering, 22(9):3233-3245.
Menezes, R., Oliveira, D., and Gomes, R. (2021). O impacto da pandemia do covid-19 no comportamento do tráfego de rede e no processo de predição. In Anais do XII Workshop de Pesquisa Experimental da Internet do Futuro, pages 25-30, Porto Alegre, RS, Brasil. SBC.
Miyazawa, K., Yamaguchi, S., and Kobayashi, A. (2020). Mechanism of cyclic performance fluctuation of tcp bbr and cubic tcp communications. In 2020 IEEE 44th Annual Computers, Software, and Applications Conference (COMPSAC), pages 1139-1144.
Mok, R. K. P., Zou, H., Yang, R., Koch, T., Katz-Bassett, E., and Claffy, K. C. (2021). Measuring the network performance of google cloud platform. In Proceedings of the 21st ACM Internet Measurement Conference, IMC '21, page 54-61, New York, NY, USA. Association for Computing Machinery.
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.
Rafi, S. H., Nahid-Al-Masood, Deeba, S. R., and Hossain, E. (2021). A short-term load forecasting method using integrated cnn and lstm network. IEEE Access, 9:32436-32448.
Sone, S. P., Lehtomäki, J. J., and Khan, Z. (2020). Wireless traffic usage forecasting using real enterprise network data: Analysis and methods. IEEE Open Journal of the Communications Society, 1:777-797.
Syu, Y., Wang, C., and Fanjiang, Y. (2019). Modeling and forecasting of time-aware dynamic qos attributes for cloud services. IEEE Transactions on Network and Service Management, 16(1):56-71.
Tomic, I., Bleakley, E., and Ivanis, P. (2022). Predictive capacity planning for mobile networksmdash;ml supported prediction of network performance and user experience evolution. Electronics, 11(4).
Yang, H., Li, X., Qiang, W., Zhao, Y., Zhang, W., and Tang, C. (2021). A network traffic forecasting method based on sa optimized arima-bp neural network. Computer Networks, 193:108102.
Publicado
22/05/2023
Como Citar
RIBEIRO, Silvio E. S. B.; MENEZES, Rafael A.; PORTELA, Ariel L. C.; ARAÚJO, Thelmo P.; GOMES, Rafael L..
Aplicando Redes Neurais e Análise Temporal para Predição Adaptativa de Desempenho de Rede. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 41. , 2023, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2023
.
p. 490-503.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2023.508.
