Identification of Causes of High Latency Situations in Internet Service Providers
Abstract
Internet Service Providers (ISPs) offer network monitoring services that include regular performance tests, with end-to-end delay being a critical piece of information they provide. Nevertheless, the monitoring tools still need to evolve to encompass more complex activities, such as high-delay detection. Within this context, this paper presents a method to detect high delays in communication links in the network infrastructure using the proposed Impact Score, based on data correlation techniques, over data from network monitoring tools. The experiments, using real data from the National Education and Research Network (RNP), show that the proposed method is capable of indicating the network links that compromise the end-to-end delay.References
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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.
Published
2024-07-21
How to Cite
SILVA, Danielle S.; NOBRE, Francisco V. J.; FERREIRA, Maria C.; PORTELA, Ariel L.; ARAÚJO, Thelmo P.; GOMES, Rafael L..
Identification of Causes of High Latency Situations in Internet Service Providers. In: PROCEEDINGS OF BRAZILIAN SYMPOSIUM ON UBIQUITOUS AND PERVASIVE COMPUTING (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.
