Aplicando Decomposição de Valores Singulares na Predição de Vazão de Rede
Resumo
Diversas empresas e provedores de internet (ISPs) realizam serviços de monitoramento de rede para compreender o comportamento da rede e obter dados relevantes para o planejamento estratégico. No entanto, falhas podem ocorrer durante a realização dessas medições, principalmente nas medições de vazão, dificultando a execução de soluções de predição do desempenho da rede. Dentro deste contexto, este artigo propõe uma abordagem que combina imputação de dados com predição para melhorar a qualidade das análises de vazão. A solução baseia-se na técnica de decomposição de valores singulares (SVD) para realizar a imputação de dados, explorando aspectos sazonais das séries temporais que ditam o desempenho de vazão da rede. Após a imputação, as séries são utilizadas em redes neurais recorrentes para predições com alta eficiência. Nos experimentos realizados, usando dados reais do Serviço de Monitoramento da Rede Ipê (Monipê), foi validada a eficácia do modelo proposto, com eficiência superior a soluções existentes.
Palavras-chave:
Imputação de Dados Temporais, Predição de Vazão de Rede, Singular Value Decomposition (SVD), Redes Neurais Recorrentes
Referências
Ahn, H., Sun, K., and Kim, K. (2021). Comparison of missing data imputation methods in time series forecasting. Computers, Materials and Continua, 70, 767–779.
Anil Jadhav, D. P. and Ramanathan, K. (2019). Comparison of performance of data imputation methods for numeric dataset. Applied Artificial Intelligence, 33(10), 913–933.
Cho, B., Dayrit, T., Gao, Y., Wang, Z., Hong, T., Sim, A., and Wu, K. (2020). Effective missing value imputation methods for building monitoring data. In 2020 IEEE International Conference on Big Data (Big Data), 2866–2875.
Ding, Z., Mei, G., Cuomo, S., Li, Y., and Xu, N. (2020). Comparison of estimating missing values in IoT time series data using different interpolation algorithms. International Journal of Parallel Programming, 48(3), 534–548.
Ferreira, M. C., Ribeiro, S. E., Nobre, F. V., Linhares, M. L., Araujo, T. P., and Gomes, R. L. (2024). Mitigating measurement failures in throughput performance forecasting. In 2024 20th International Conference on Network and Service Management (CNSM), 1–7.
García-Peña, M., Arciniegas Alarcón, S., Krzanowski, W., and Duarte Vogel, D. (2021). Missing-value imputation using the robust singular-value decomposition: Proposals and numerical evaluation. Crop Science, 61.
Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. M. (2014a). A similarity model for virtual networks negotiation. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC ’14, 489–494. New York, NY, USA: Association for Computing Machinery.
Gomes, R. L., Bittencourt, L. F., Madeira, E. R. M., Cerqueira, E., and Gerla, M. (2014b). An architecture for dynamic resource adjustment in VSDNs based on traffic demand. In 2014 IEEE Global Communications Conference, 2005–2010.
Li, H. (2021). Time works well: Dynamic time warping based on time weighting for time series data mining. Information Sciences, 547, 592–608.
Naf, J., Spohn, M.-L., Michel, L., and Meinshausen, N. (2022). Imputation scores.
Park, J., Muller, J., Arora, B., Faybishenko, B., Pastorello, G., Varadharajan, C., Sahu, R., and Agarwal, D. (2023). Long-term missing value imputation for time series data using deep neural networks. Neural Computing and Applications, 35(12), 9071–9091.
Phan, T.-T.-H. (2020). Machine learning for univariate time series imputation. In 2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), 1–6.
Pinheiro, B., Nascimento, V., Gomes, R., Cerqueira, E., and Abelem, A. (2011). A multimedia-based fuzzy queue-aware routing approach for wireless mesh networks. In 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN), 1–7.
Portela, A., Linhares, M. M., Nobre, F. V. J., Menezes, R., Mesquita, M., and Gomes, R. L. (2024a). The role of TCP congestion control in the throughput forecasting. In Proceedings of the 13th Latin-American Symposium on Dependable and Secure Computing, LADC ’24, 196–199. New York, NY, USA: Association for Computing Machinery.
Portela, A. L., Menezes, R. A., Costa, W. L., Silveira, M. M., Bittecnourt, L. F., and Gomes, R. L. (2023). Detection of IoT devices and network anomalies based on anonymized network traffic. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, 1–6.
Portela, A. L. C., Ribeiro, S. E. S. B., Menezes, R. A., de Araujo, T., and Gomes, R. L. (2024b). T-for: An adaptable forecasting model for throughput performance. IEEE Transactions on Network and Service Management, 1–1.
Sandra Taylor, Matthew Ponzini, M. W. and Kim, K. (2022). Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data. Briefings in Bioinformatics, 23(1), 913–933.
Silva, M., Ribeiro, S., Carvalho, V., Cardoso, F., and Gomes, R. L. (2023). Scalable detection of SQL injection in cyber-physical systems. In Proceedings of the 12th Latin-American Symposium on Dependable and Secure Computing, LADC ’23, 220–225. New York, NY, USA: Association for Computing Machinery.
Silva, M. V., Mosca, E. E., and Gomes, R. L. (2022). Green industrial Internet of Things through data compression. International Journal of Embedded Systems, 15(6), 457–466.
Silveira, M. M., Portela, A. L., Menezes, R. A., Souza, M. S., Silva, D. S., Mesquita, M. C., and Gomes, R. L. (2023a). Data protection based on searchable encryption and anonymization techniques. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, 1–5.
Silveira, M. M., Silva, D. S., Rodriguez, S. J. R., and Gomes, R. L. (2023b). Searchable symmetric encryption for private data protection in cloud environments. In Proceedings of the 11th Latin-American Symposium on Dependable Computing, LADC ’22, 95–98. New York, NY, USA: Association for Computing Machinery.
Spiliotis, E., Assimakopoulos, V., and Makridakis, S. (2020). Generalizing the theta method for automatic forecasting. European Journal of Operational Research, 284(2), 550–558.
Vasileva, P., McKee, S., Penev, A., and Vukotic, I. (2021). PS-DASH – analysis, monitoring and visualization of network measurements. In 2021 International Conference Automatics and Informatics (ICAI), 93–96.
Yamak, P. T., Yujian, L., and Gadosey, P. K. (2020). A comparison between ARIMA, LSTM, and GRU for time series forecasting. In Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI ’19, 49–55. New York, NY, USA: Association for Computing Machinery.
Anil Jadhav, D. P. and Ramanathan, K. (2019). Comparison of performance of data imputation methods for numeric dataset. Applied Artificial Intelligence, 33(10), 913–933.
Cho, B., Dayrit, T., Gao, Y., Wang, Z., Hong, T., Sim, A., and Wu, K. (2020). Effective missing value imputation methods for building monitoring data. In 2020 IEEE International Conference on Big Data (Big Data), 2866–2875.
Ding, Z., Mei, G., Cuomo, S., Li, Y., and Xu, N. (2020). Comparison of estimating missing values in IoT time series data using different interpolation algorithms. International Journal of Parallel Programming, 48(3), 534–548.
Ferreira, M. C., Ribeiro, S. E., Nobre, F. V., Linhares, M. L., Araujo, T. P., and Gomes, R. L. (2024). Mitigating measurement failures in throughput performance forecasting. In 2024 20th International Conference on Network and Service Management (CNSM), 1–7.
García-Peña, M., Arciniegas Alarcón, S., Krzanowski, W., and Duarte Vogel, D. (2021). Missing-value imputation using the robust singular-value decomposition: Proposals and numerical evaluation. Crop Science, 61.
Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. M. (2014a). A similarity model for virtual networks negotiation. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC ’14, 489–494. New York, NY, USA: Association for Computing Machinery.
Gomes, R. L., Bittencourt, L. F., Madeira, E. R. M., Cerqueira, E., and Gerla, M. (2014b). An architecture for dynamic resource adjustment in VSDNs based on traffic demand. In 2014 IEEE Global Communications Conference, 2005–2010.
Li, H. (2021). Time works well: Dynamic time warping based on time weighting for time series data mining. Information Sciences, 547, 592–608.
Naf, J., Spohn, M.-L., Michel, L., and Meinshausen, N. (2022). Imputation scores.
Park, J., Muller, J., Arora, B., Faybishenko, B., Pastorello, G., Varadharajan, C., Sahu, R., and Agarwal, D. (2023). Long-term missing value imputation for time series data using deep neural networks. Neural Computing and Applications, 35(12), 9071–9091.
Phan, T.-T.-H. (2020). Machine learning for univariate time series imputation. In 2020 International Conference on Multimedia Analysis and Pattern Recognition (MAPR), 1–6.
Pinheiro, B., Nascimento, V., Gomes, R., Cerqueira, E., and Abelem, A. (2011). A multimedia-based fuzzy queue-aware routing approach for wireless mesh networks. In 2011 Proceedings of 20th International Conference on Computer Communications and Networks (ICCCN), 1–7.
Portela, A., Linhares, M. M., Nobre, F. V. J., Menezes, R., Mesquita, M., and Gomes, R. L. (2024a). The role of TCP congestion control in the throughput forecasting. In Proceedings of the 13th Latin-American Symposium on Dependable and Secure Computing, LADC ’24, 196–199. New York, NY, USA: Association for Computing Machinery.
Portela, A. L., Menezes, R. A., Costa, W. L., Silveira, M. M., Bittecnourt, L. F., and Gomes, R. L. (2023). Detection of IoT devices and network anomalies based on anonymized network traffic. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, 1–6.
Portela, A. L. C., Ribeiro, S. E. S. B., Menezes, R. A., de Araujo, T., and Gomes, R. L. (2024b). T-for: An adaptable forecasting model for throughput performance. IEEE Transactions on Network and Service Management, 1–1.
Sandra Taylor, Matthew Ponzini, M. W. and Kim, K. (2022). Comparison of imputation and imputation-free methods for statistical analysis of mass spectrometry data with missing data. Briefings in Bioinformatics, 23(1), 913–933.
Silva, M., Ribeiro, S., Carvalho, V., Cardoso, F., and Gomes, R. L. (2023). Scalable detection of SQL injection in cyber-physical systems. In Proceedings of the 12th Latin-American Symposium on Dependable and Secure Computing, LADC ’23, 220–225. New York, NY, USA: Association for Computing Machinery.
Silva, M. V., Mosca, E. E., and Gomes, R. L. (2022). Green industrial Internet of Things through data compression. International Journal of Embedded Systems, 15(6), 457–466.
Silveira, M. M., Portela, A. L., Menezes, R. A., Souza, M. S., Silva, D. S., Mesquita, M. C., and Gomes, R. L. (2023a). Data protection based on searchable encryption and anonymization techniques. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, 1–5.
Silveira, M. M., Silva, D. S., Rodriguez, S. J. R., and Gomes, R. L. (2023b). Searchable symmetric encryption for private data protection in cloud environments. In Proceedings of the 11th Latin-American Symposium on Dependable Computing, LADC ’22, 95–98. New York, NY, USA: Association for Computing Machinery.
Spiliotis, E., Assimakopoulos, V., and Makridakis, S. (2020). Generalizing the theta method for automatic forecasting. European Journal of Operational Research, 284(2), 550–558.
Vasileva, P., McKee, S., Penev, A., and Vukotic, I. (2021). PS-DASH – analysis, monitoring and visualization of network measurements. In 2021 International Conference Automatics and Informatics (ICAI), 93–96.
Yamak, P. T., Yujian, L., and Gadosey, P. K. (2020). A comparison between ARIMA, LSTM, and GRU for time series forecasting. In Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, ACAI ’19, 49–55. New York, NY, USA: Association for Computing Machinery.
Publicado
19/05/2025
Como Citar
FERREIRA, Maria C. M. M.; LINHARES, Maria L.; ARAÚJO, Thelmo P.; GOMES, Rafael L..
Aplicando Decomposição de Valores Singulares na Predição de Vazão de Rede. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 43. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 630-643.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2025.6334.
