Identificação das Causas de Situações de Alto Atraso em Provedores de Internet
Resumo
Provedores de Internet (ISPs) oferecem serviços de monitoramento de rede que incluem testes regulares de desempenho, sendo o atraso fim-a-fim uma informação crucial. Contudo, as ferramentas de monitoramento ainda precisam evoluir para abranger atividades mais complexas, como a detecção de situações de atraso alto. Dentro deste contexto, este trabalho apresenta um método para detectar situações de atraso alto em enlaces de comunicação na infraestrutura de rede utilizando um Score de Impacto proposto, baseado em técnicas de correlação de dados, sobre dados de ferramentas de monitoramento de rede. Os experimentos, utilizando dados reais da Rede Nacional de Ensino e Pesquisa (RNP), mostram que o método proposto é capaz de indicar os enlaces da rede que comprometem o atraso ponta a ponta.Referências
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Kim, Y., Kim, T.-H., and Ergün, T. (2015). The instability of the pearson correlation coefficient in the presence of coincidental outliers. Finance Research Letters, 13:243–257.
Li, W., Wang, X., Zhang, Y., and Wu, Q. (2021). Traffic flow prediction over muti-sensor data correlation with graph convolution network. Neurocomputing, 427:50–63.
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.
Moreira, D. A., Marques, H. P., Costa, W. L., Celestino, J., Gomes, R. L., and Nogueira, M. (2021). Anomaly detection in smart environments using ai over fog and cloud computing. In 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), pages 1–2. IEEE.
Pires, S. and Mascarenhas, C. (2023). Cyber threat analysis using pearson and spearman correlation via exploratory data analysis. In 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC), pages 257–262.
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, pages 1–6.
Portela, A. L. C., Ribeiro, S. E. S. B., Menezes, R. A., de Araujo, T., and Gomes, R. L. (2024). T-for: An adaptable forecasting model for throughput performance. IEEE Transactions on Network and Service Management, pages 1–1.
Scarpitta, C., Sidoretti, G., Mayer, A., Salsano, S., Abdelsalam, A., and Filsfils, C. (2023). High performance delay monitoring for srv6 based sd-wans. IEEE Transactions on Network and Service Management, pages 1–1.
Silveira, M. M., Portela, A. L., Menezes, R. A., Souza, M. S., Silva, D. S., Mesquita, M. C., and Gomes, R. L. (2023). Data protection based on searchable encryption and anonymization techniques. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, pages 1–5.
Wang, B., Lun, S., Li, M., and Lu, X. (2024). Echo state network structure optimization algorithm based on correlation analysis. Applied Soft Computing, 152:111214.
BinSahaq, A., Sheltami, T., Mahmoud, A., and Nasser, N. (2022). Fast and efficient algorithm for delay-sensitive qos provisioning in sdn networks. Wireless Networks, pages 1–22.
Costa, W. L., Portela, A. L., and Gomes, R. L. (2021). Features-aware ddos detection in heterogeneous smart environments based on fog and cloud computing. International Journal of Communication Networks and Information Security, 13(3):491–498.
da Silva, G., Oliveira, D., Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. M. (2020). Reliable network slices based on elastic network resource demand. In NOMS 2020 2020 IEEE/IFIP Network Operations and Management Symposium, pages 1–9.
Gomes, R. L., Bittencourt, L. F., and Madeira, E. R. (2020). Reliability-aware network slicing in elastic demand scenarios. IEEE Communications Magazine, 58(10):29–34.
Gottwalt, F., Chang, E., and Dillon, T. (2019). Corrcorr: A feature selection method for multivariate correlation network anomaly detection techniques. Computers & Security, 83:234–245.
Imran, Zuhairi, M. F. A., Ali, S. M., Shahid, Z., Alam, M. M., and Su’ud, M. M. (2023). Improving reliability for detecting anomalies in the mqtt network by applying correlation analysis for feature selection using machine learning techniques. Applied Sciences, 13(11).
Kim, Y., Kim, T.-H., and Ergün, T. (2015). The instability of the pearson correlation coefficient in the presence of coincidental outliers. Finance Research Letters, 13:243–257.
Li, W., Wang, X., Zhang, Y., and Wu, Q. (2021). Traffic flow prediction over muti-sensor data correlation with graph convolution network. Neurocomputing, 427:50–63.
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.
Moreira, D. A., Marques, H. P., Costa, W. L., Celestino, J., Gomes, R. L., and Nogueira, M. (2021). Anomaly detection in smart environments using ai over fog and cloud computing. In 2021 IEEE 18th Annual Consumer Communications & Networking Conference (CCNC), pages 1–2. IEEE.
Pires, S. and Mascarenhas, C. (2023). Cyber threat analysis using pearson and spearman correlation via exploratory data analysis. In 2023 Third International Conference on Secure Cyber Computing and Communication (ICSCCC), pages 257–262.
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, pages 1–6.
Portela, A. L. C., Ribeiro, S. E. S. B., Menezes, R. A., de Araujo, T., and Gomes, R. L. (2024). T-for: An adaptable forecasting model for throughput performance. IEEE Transactions on Network and Service Management, pages 1–1.
Scarpitta, C., Sidoretti, G., Mayer, A., Salsano, S., Abdelsalam, A., and Filsfils, C. (2023). High performance delay monitoring for srv6 based sd-wans. IEEE Transactions on Network and Service Management, pages 1–1.
Silveira, M. M., Portela, A. L., Menezes, R. A., Souza, M. S., Silva, D. S., Mesquita, M. C., and Gomes, R. L. (2023). Data protection based on searchable encryption and anonymization techniques. In NOMS 2023-2023 IEEE/IFIP Network Operations and Management Symposium, pages 1–5.
Wang, B., Lun, S., Li, M., and Lu, X. (2024). Echo state network structure optimization algorithm based on correlation analysis. Applied Soft Computing, 152:111214.
Publicado
21/07/2024
Como Citar
SILVA, Danielle S.; NOBRE, Francisco V. J.; FERREIRA, Maria C.; PORTELA, Ariel L.; ARAÚJO, Thelmo P.; GOMES, Rafael L..
Identificação das Causas de Situações de Alto Atraso em Provedores de Internet. In: SIMPÓSIO BRASILEIRO DE COMPUTAÇÃO UBÍQUA E PERVASIVA (SBCUP), 16. , 2024, Brasília/DF.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 111-120.
ISSN 2595-6183.
DOI: https://doi.org/10.5753/sbcup.2024.2881.
