Correlacionando Dados de Monitoramento de Rede para Identificação de Causas de Problemas de Desempenho
Resumo
Várias empresas e provedores de Internet (ISPs) possuem serviços de monitoramento de rede que abrangem avaliações regulares de desempenho, com foco principal na entrega de informações cruciais sobre a situação atual da infraestrutura de rede e, consequentemente, dos serviços que executam sobre ela. No entanto, estas ferramentas de monitoramento precisam de um desenvolvimento contínuo para incorporar tarefas mais complexas, como a detecção de problemas de desempenho. Dentro deste contexto, este artigo apresenta um mecanismo para identificar atrasos altos e os links de comunicação da rede que podem ser a causa desses problemas de desempenho, utilizando um Score de impacto formulado considerando aspectos temporais. Esse Score baseia-se em técnicas de correlação de dados aplicadas às informações coletadas por ferramentas de monitoramento. Os experimentos realizados com dados reais da RNP demonstram a eficácia do mecanismo proposto na identificação de links de rede que impactam a comunicação de dados, gerando os atrasos altos fim-a-fim.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, page 54–61, New York, NY, USA. Association for Computing Machinery.
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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.
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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.
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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.
Gajewski, M., Mongay Batalla, J., Mastorakis, G., and Mavromoustakis, C. X. (2022). Anomaly traffic detection and correlation in smart home automation iot systems. Transactions on Emerging Telecommunications Technologies, 33(6):e4053.
Gomes, R., Bittencourt, L., Madeira, E., Cerqueira, E., and Gerla, M. (2017). Management of virtual network resources for multimedia applications. Multimedia Systems, 23(4):405–419.
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.
Gomes, R. L., Bittencourt, L. F., Madeira, E. R., Cerqueira, E., and Gerla, M. (2016). A combined energy-bandwidth approach to allocate resilient virtual software defined networks. Journal of Network and Computer Applications, 69:98–106.
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
24/05/2024
Como Citar
SILVA, Danielle S.; NOBRE, Francisco V. J.; FERREIRA, Maria C.; ARAÚJO, Thelmo P.; GOMES, Rafael L..
Correlacionando Dados de Monitoramento de Rede para Identificação de Causas de Problemas de Desempenho. In: WORKSHOP DE GERÊNCIA E OPERAÇÃO DE REDES E SERVIÇOS (WGRS), 29. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 15-28.
ISSN 2595-2722.
DOI: https://doi.org/10.5753/wgrs.2024.2892.
