Time-Weighted Correlation Approach to Identify High Delay Links in Internet Service Providers
DOI:
https://doi.org/10.5753/jisa.2025.5218Keywords:
Correlation, Data Analysis, Network PerformanceAbstract
Companies and Internet Service Providers (ISPs) apply monitoring tools over network infrastructure, encompassing regular performance evaluations, with a primary focus on delivering crucial information about the current state of the network infrastructure and, consequently, the services running on it. However, these monitoring tools require ongoing development to handle more complex tasks, such as detecting performance issues. Within this context, this article proposes a mechanism for identifying high delays and communication links in the network that may cause these performance issues, using a temporally formulated Impact Score. This Score is based on data correlation techniques applied to information collected by monitoring tools. Experiments conducted with real data from the RNP Network indicate the efficiency of the proposal in identifying links impacting data communication, resulting in high end-to-end delays.
Downloads
References
Alam, M. I. I., Anshelevich, E., Kar, K., and Yuksel, M. (2024). Pricing for efficient traffic exchange at ixps. IEEE/ACM Transactions on Networking, 32(3):2053-2068. DOI: 10.1109/TNET.2023.3336352.
Arachchige, K. G., Branch, P., and But, J. (2023). Evaluation of correlation between temperature of iot microcontroller devices and blockchain energy consumption in wireless sensor networks. Sensors, 23(14). DOI: 10.3390/s23146265.
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. DOI: 10.1007/s11276-022-03028-3.
Costa, M. A., Costa, Y. M., Almeida, Y. O., Cardoso, F. J., and Gomes, R. L. (2024). Connection management using automated firewall based on threat intelligence. In Proceedings of the 2024 Latin America Networking Conference, LANC '24, page 32–37, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3685323.3685331.
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. DOI: 10.23919/CNSM62983.2024.10814394.
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. DOI: 10.1002/ett.4053.
Guo, J., Ning, X., Hua, C., Yang, J., and Cai, Z. (2025). A path statistical delay prediction framework based on global graph neural network. IEEE Transactions on Circuits and Systems I: Regular Papers, pages 1-13. DOI: 10.1109/TCSI.2025.3542438.
Hekmati, A., Zhang, J., Sarkar, T., Jethwa, N., Grippo, E., and Krishnamachari, B. (2024). Correlation-aware neural networks for ddos attack detection in iot systems. IEEE/ACM Transactions on Networking, 32(5):3929-3944. DOI: 10.1109/TNET.2024.3408675.
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). DOI: 10.3390/app13116753.
Kim, J., Moon, Y., and Ko, H. (2024). Correlation-based advanced feature analysis for wireless sensor networks. The Journal of Supercomputing, 80(7):9812-9828. DOI: 10.1007/s11227-023-05739-6.
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. DOI: 10.1016/j.frl.2014.12.005.
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. DOI: 10.1016/j.neucom.2020.11.032.
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. DOI: 10.1145/3487552.3487862.
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, page 196–199, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3697090.3699869.
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. DOI: 10.1109/TNSM.2024.3349701.
Sawabe, A., Shinohara, Y., and Iwai, T. (2024). Rethinking delay behavior in mobile networks as a lifeline of industrial applications. In GLOBECOM 2024 - 2024 IEEE Global Communications Conference, pages 1803-1808. DOI: 10.1109/GLOBECOM52923.2024.10901521.
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. DOI: 10.1109/TNSM.2023.3300151.
Silva, D., Nobre, F., Ferreira, M., Portela, A., Araújo, T., and Gomes, R. (2024). Identificação das causas de situações de alto atraso em provedores de internet. In Anais do XVI Simpósio Brasileiro de Computação Ubíqua e Pervasiva, pages 111-120, Porto Alegre, RS, Brasil. SBC. DOI: 10.5753/sbcup.2024.2881.
Silveira, M., Santos, D., Souza, M., Silva, D., Mesquita, M., Neto, J., and Gome, R. L. (2023). An anonymization service for privacy in data mining. In Proceedings of the 12th Latin-American Symposium on Dependable and Secure Computing, LADC '23, page 214–219, New York, NY, USA. Association for Computing Machinery. DOI: 10.1145/3615366.3625074.
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. DOI: 10.5753/jisa.2024.3799.
Stafecka, A., Lizunovs, A., Olins, A., Rjumsins, M., Spolitis, S., and Bobrovs, V. (2024). Defining internet access service qos measurement server placement criteria in national internet network. In 2024 Photonics & Electromagnetics Research Symposium (PIERS), pages 1-6. DOI: 10.1109/PIERS62282.2024.10617842.
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. DOI: 10.1016/j.asoc.2023.111214.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Journal of Internet Services and Applications

This work is licensed under a Creative Commons Attribution 4.0 International License.

