Estimativa de Vazão de Rede Baseada em Técnicas de Regressão sobre Dados de Monitoramento
Resumo
Empresas e provedores de internet (ISPs) costumam realizar serviços de monitoramento de rede, fornecendo resultados de testes regulares de desempenho, como vazão, perda, traceroute, atraso, entre outros. As medições de vazão, diferente das demais, são realizadas em intervalos longos, visto que consomem muitos recursos de rede e afetam a Qualidade de Serviço (QoS) e de Experiência (QoE) dos usuários. Todavia, as informações de vazão são fundamentais para um gerenciamento de rede eficaz. Dentro deste contexto, este artigo apresenta uma metodologia inovadora para estimar a vazão de rede (em um determinado momento) a partir do agrupamento de outras medições que não comprometem a QoS e QoE dos usuários, tais como traceroute e atraso. Os experimentos, realizados com dados reais da Rede Nacional de Ensino e Pesquisa (RNP), demonstram que a integração de medições complementares aumenta a precisão das estimativas.
Palavras-chave:
vazão de rede, regressão, monitoramento de rede
Referências
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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, pages 54-61.
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Portela, A. L., Menezes, R. A., Costa, W. L., Silveira, M. M., Bittencourt, 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, pages 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, pages 1-1.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A. (2018). Catboost: Unbiased boosting with categorical features. In Advances in Neural Information Processing Systems, volume 31, pages 6638-6648.
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, pages 220-225.
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, pages 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, pages 95-98.
Souza, M. S., Ribeiro, S. E. S. B., Lima, V. C., Cardoso, F. J., and Gomes, R. L. (2024). Combining regular expressions and machine learning for SQL injection detection in urban computing. Journal of Internet Services and Applications, 15(1):103-111.
Yang, H., Li, X., Qiang, W., Zhao, Y., Zhang, W., and Tang, C. (2021a). A network traffic forecasting method based on SA optimized ARIMA-BP neural network. Computer Networks, 193:108102.
Yang, X., Wang, X., Li, Z., Liu, Y., Qian, F., Gong, L., Miao, R., and Xu, T. (2021b). Fast and light bandwidth testing for Internet users. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21), pages 1011-1026.
Breiman, L. (2001). Random forests. Machine Learning, 45(1):5-32.
Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 785-794.
Cover, T. and Hart, P. (1967). Nearest neighbor pattern classification. IEEE Transactions on Information Theory, 13(1):21-27.
Damaskinos, G., Guerraoui, R., Kermarrec, A.-M., Nitu, V., Patra, R., and Taiani, F. (2022). Fleet: Online federated learning via staleness awareness and performance prediction. ACM Transactions on Intelligent Systems and Technology, 13(5).
Ferreira, M. C., Ribeiro, S. E., Nobre, F. V., Linhares, M. L., Araújo, 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), pages 1-7.
Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29(5):1189-1232.
Gijon, C., Mahmoodi, T., Toril, M., Luna-Ramirez, S., and Bejarano-Luque, J. (2024). Sla-driven traffic steering in b5g systems with network slicing. IEEE Transactions on Vehicular Technology.
Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. (2014a). A bandwidth-feasibility algorithm for reliable virtual network allocation. In 2014 IEEE 28th International Conference on Advanced Information Networking and Applications, pages 504-511.
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, pages 2005-2010.
Liang, W., Li, Y., Xu, J., Qin, Z., Zhang, D., and Li, K.-C. (2023). Qos prediction and adversarial attack protection for distributed services under Dlaas. IEEE Transactions on Computers, pages 1-1.
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, pages 54-61.
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, pages 196-199.
Portela, A. L., Menezes, R. A., Costa, W. L., Silveira, M. M., Bittencourt, 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, pages 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, pages 1-1.
Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., and Gulin, A. (2018). Catboost: Unbiased boosting with categorical features. In Advances in Neural Information Processing Systems, volume 31, pages 6638-6648.
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, pages 220-225.
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, pages 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, pages 95-98.
Souza, M. S., Ribeiro, S. E. S. B., Lima, V. C., Cardoso, F. J., and Gomes, R. L. (2024). Combining regular expressions and machine learning for SQL injection detection in urban computing. Journal of Internet Services and Applications, 15(1):103-111.
Yang, H., Li, X., Qiang, W., Zhao, Y., Zhang, W., and Tang, C. (2021a). A network traffic forecasting method based on SA optimized ARIMA-BP neural network. Computer Networks, 193:108102.
Yang, X., Wang, X., Li, Z., Liu, Y., Qian, F., Gong, L., Miao, R., and Xu, T. (2021b). Fast and light bandwidth testing for Internet users. In 18th USENIX Symposium on Networked Systems Design and Implementation (NSDI 21), pages 1011-1026.
Publicado
19/05/2025
Como Citar
LINHARES, Maria L.; FERREIRA, Maria C. M. M.; ARAÚJO, Thelmo P.; IMMICH, Roger; GOMES, Rafael L..
Estimativa de Vazão de Rede Baseada em Técnicas de Regressão sobre Dados de Monitoramento. In: WORKSHOP DE GERÊNCIA E OPERAÇÃO DE REDES E SERVIÇOS (WGRS), 30. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 57-70.
ISSN 2595-2722.
DOI: https://doi.org/10.5753/wgrs.2025.8765.
