Algoritmos de Controle de Congestionamento Justos e Eficientes para Aplicações TCP em Redes Sensíveis a Perda de Pacotes e a Atrasos
Abstract
As the number of devices connected to the Internet increases, the response of applications to competition for network resources and, consequently, to congestion has become a critical issue that affects service quality and user experience. Next-generation networks, such as beyond 5G, 6G, and low Earth orbit (LEO) satellite networks, face additional challenges due to their high sensitivity to delays and packet losses. This paper proposes three TCP congestion control algorithms (CCA) models, named Expolinear, Fractional, and Linear, which leverage packet loss information to dynamically adjust the congestion window (CWND), improving efficiency and adaptability to varying network conditions. We have implemented the algorithms as Linux kernel modules and compared them to traditional algorithms. The proposed models demonstrated up to ten times greater transmission rate efficiency in high packet loss scenarios while maintaining fairness in bandwidth usage among competing flows.
Keywords:
TCP congestion control, Next-generation networks, Packet loss, Congestion window, Transmission efficiency
References
Addanki, V., Apostolaki, M., Ghobadi, M., Schmid, S., & Vanbever, L. (2022). ABM: Active buffer management in datacenters. In Proceedings of the ACM SIGCOMM 2022 Conference, SIGCOMM ’22 (pp. 36–52). New York, NY, USA: Association for Computing Machinery.
Akhtar, Z., Nam, Y. S., Govindan, R., Rao, S., Chen, J., Katz-Bassett, E., Ribeiro, B., Zhan, J., & Zhang, H. (2018). Oboe: Auto-tuning video ABR algorithms to network conditions. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, SIGCOMM ’18 (pp. 44–58). New York, NY, USA: Association for Computing Machinery.
Arslan, S., Li, Y., Kumar, G., & Dukkipati, N. (2023). Bolt: Sub-RTT congestion control for ultra-low latency. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23) (pp. 219–236). Boston, MA: USENIX Association.
Brakmo, L. S., O’Malley, S. W., & Peterson, L. L. (1994). TCP Vegas: New techniques for congestion detection and avoidance. In Proceedings of the Conference on Communications Architectures, Protocols and Applications, SIGCOMM ’94 (pp. 24–35). New York, NY, USA: Association for Computing Machinery.
Budhiraja, I., Kumar, N., Tyagi, S., Tanwar, S., Han, Z., Piran, M. J., & Suh, D. Y. (2021). A systematic review on NOMA variants for 5G and beyond. IEEE Access, 9, 85573–85644.
Cardwell, N., Cheng, Y., Gunn, C. S., Yeganeh, S. H., & Jacobson, V. (2016). BBR: Congestion-based congestion control. ACM Queue, 14, September–October, 20–53.
Cho, I., Jang, K., & Han, D. (2017). Credit-scheduled delay-bounded congestion control for datacenters. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication, SIGCOMM ’17 (pp. 239–252). New York, NY, USA: Association for Computing Machinery.
Conde, J., Martínez, G., Reviriego, P., & Hernandez, J. A. (2024). Round trip times (RTTs): Comparing terrestrial and LEO satellite networks. In 2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN) (pp. 42–46).
Ha, S., Rhee, I., & Xu, L. (2008). CUBIC: A new TCP-friendly high-speed TCP variant. SIGOPS Operating Systems Review, 42(5), 64–74.
Hegde, P., de Veciana, G., & Mokhtari, A. (2023). Network adaptive federated learning: Congestion and lossy compression.
Hemminger, S., et al. (2005). Network emulation with NetEm. In Linux.conf.au (Vol. 5, pp. 2005).
Li, W., Wu, Z., Zhang, B., Zheng, L., Zhao, W., Zou, J., & Sun, S. (2024). In 2024 IEEE 30th International Conference on Telecommunications (ICT) (pp. 01–06).
Padhye, J., Firoiu, V., Towsley, D. F., & Kurose, J. F. (2000). Modeling TCP Reno performance: A simple model and its empirical validation. IEEE/ACM Transactions on Networking, 8(2), 133–145.
Saltzer, J. H., Reed, D. P., & Clark, D. D. (1984). End-to-end arguments in system design. ACM Transactions on Computer Systems, 2(4), 277–288.
Winstein, K., & Balakrishnan, H. (2013). TCP ex machina: Computer-generated congestion control. SIGCOMM Computer Communication Review, 43(4), 123–134.
Xiao, K., Mao, S., & Tugnait, J. K. (2019). TCP-DRINC: Smart congestion control based on deep reinforcement learning. IEEE Access, 7, 11892–11904.
Yang, W., Liu, Y., Tian, C., Jiang, J., & Guo, L. (2023). Gemini: Divide-and-conquer for practical learning-based internet congestion control. In IEEE INFOCOM 2023 – IEEE Conference on Computer Communications (pp. 1–10).
Yen, C.-Y., Abbasloo, S., & Chao, H. J. (2023). Computers can learn from the heuristic designs and master internet congestion control. In Proceedings of the ACM SIGCOMM 2023 Conference, ACM SIGCOMM ’23 (pp. 255–274). New York, NY, USA: Association for Computing Machinery.
Zhang, J., & Yeh, E. (2024). Congestion-aware routing and content placement in elastic cache networks. In IEEE INFOCOM 2024 – IEEE Conference on Computer Communications (pp. 1471–1480).
Akhtar, Z., Nam, Y. S., Govindan, R., Rao, S., Chen, J., Katz-Bassett, E., Ribeiro, B., Zhan, J., & Zhang, H. (2018). Oboe: Auto-tuning video ABR algorithms to network conditions. In Proceedings of the 2018 Conference of the ACM Special Interest Group on Data Communication, SIGCOMM ’18 (pp. 44–58). New York, NY, USA: Association for Computing Machinery.
Arslan, S., Li, Y., Kumar, G., & Dukkipati, N. (2023). Bolt: Sub-RTT congestion control for ultra-low latency. In 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI 23) (pp. 219–236). Boston, MA: USENIX Association.
Brakmo, L. S., O’Malley, S. W., & Peterson, L. L. (1994). TCP Vegas: New techniques for congestion detection and avoidance. In Proceedings of the Conference on Communications Architectures, Protocols and Applications, SIGCOMM ’94 (pp. 24–35). New York, NY, USA: Association for Computing Machinery.
Budhiraja, I., Kumar, N., Tyagi, S., Tanwar, S., Han, Z., Piran, M. J., & Suh, D. Y. (2021). A systematic review on NOMA variants for 5G and beyond. IEEE Access, 9, 85573–85644.
Cardwell, N., Cheng, Y., Gunn, C. S., Yeganeh, S. H., & Jacobson, V. (2016). BBR: Congestion-based congestion control. ACM Queue, 14, September–October, 20–53.
Cho, I., Jang, K., & Han, D. (2017). Credit-scheduled delay-bounded congestion control for datacenters. In Proceedings of the Conference of the ACM Special Interest Group on Data Communication, SIGCOMM ’17 (pp. 239–252). New York, NY, USA: Association for Computing Machinery.
Conde, J., Martínez, G., Reviriego, P., & Hernandez, J. A. (2024). Round trip times (RTTs): Comparing terrestrial and LEO satellite networks. In 2024 27th Conference on Innovation in Clouds, Internet and Networks (ICIN) (pp. 42–46).
Ha, S., Rhee, I., & Xu, L. (2008). CUBIC: A new TCP-friendly high-speed TCP variant. SIGOPS Operating Systems Review, 42(5), 64–74.
Hegde, P., de Veciana, G., & Mokhtari, A. (2023). Network adaptive federated learning: Congestion and lossy compression.
Hemminger, S., et al. (2005). Network emulation with NetEm. In Linux.conf.au (Vol. 5, pp. 2005).
Li, W., Wu, Z., Zhang, B., Zheng, L., Zhao, W., Zou, J., & Sun, S. (2024). In 2024 IEEE 30th International Conference on Telecommunications (ICT) (pp. 01–06).
Padhye, J., Firoiu, V., Towsley, D. F., & Kurose, J. F. (2000). Modeling TCP Reno performance: A simple model and its empirical validation. IEEE/ACM Transactions on Networking, 8(2), 133–145.
Saltzer, J. H., Reed, D. P., & Clark, D. D. (1984). End-to-end arguments in system design. ACM Transactions on Computer Systems, 2(4), 277–288.
Winstein, K., & Balakrishnan, H. (2013). TCP ex machina: Computer-generated congestion control. SIGCOMM Computer Communication Review, 43(4), 123–134.
Xiao, K., Mao, S., & Tugnait, J. K. (2019). TCP-DRINC: Smart congestion control based on deep reinforcement learning. IEEE Access, 7, 11892–11904.
Yang, W., Liu, Y., Tian, C., Jiang, J., & Guo, L. (2023). Gemini: Divide-and-conquer for practical learning-based internet congestion control. In IEEE INFOCOM 2023 – IEEE Conference on Computer Communications (pp. 1–10).
Yen, C.-Y., Abbasloo, S., & Chao, H. J. (2023). Computers can learn from the heuristic designs and master internet congestion control. In Proceedings of the ACM SIGCOMM 2023 Conference, ACM SIGCOMM ’23 (pp. 255–274). New York, NY, USA: Association for Computing Machinery.
Zhang, J., & Yeh, E. (2024). Congestion-aware routing and content placement in elastic cache networks. In IEEE INFOCOM 2024 – IEEE Conference on Computer Communications (pp. 1471–1480).
Published
2025-05-19
How to Cite
DA COSTA MADEIRA, Marcos Vinícius; MENEZES FERRAZANI MATTOS, Diogo.
Algoritmos de Controle de Congestionamento Justos e Eficientes para Aplicações TCP em Redes Sensíveis a Perda de Pacotes e a Atrasos. In: BRAZILIAN SYMPOSIUM ON COMPUTER NETWORKS AND DISTRIBUTED SYSTEMS (SBRC), 43. , 2025, Natal/RN.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2025
.
p. 504-517.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2025.6290.
