Redes de Aprendizado Profundo para Classificação e Controle de Congestionamento em Redes TCP/IP
Resumo
O avanço e a ubiquidade das redes digitais têm transformado fundamentalmente inúmeras esferas da atividade humana. No cerne desse fenômeno, encontra-se o modelo de protocolo de controle de transmissão (TCP), cuja influência é particularmente notável no crescimento exponencial da Internet, devido à sua capacidade de transmitir de maneira flexível para qualquer dispositivo, por meio do seu avançado Controle de Congestionamento (CC). Buscando um mecanismo de CC ainda mais eficiente, este trabalho propõe a construção de redes neurais de aprendizado profundo (MLP, LSTM e CNN) para classificação do nível de congestionamento. Os resultados atestam modelos capazes de distinguir, com mais de 90% de acerto, entre momentos de alto e baixo grau de congestionamento, e, com isso, realizar a diferenciação de perdas por congestionamento versus aleatórias, podendo elevar a vazão em até cinco vezes em ambientes de perdas aleatórias, quando combinado com algoritmos de CC.Referências
Abbasloo, S., Yen, C.-Y., and Chao, H. J. (2020). Classic Meets Modern: a Pragmatic Learning-Based Congestion Control for the Internet. In Proceedings of the Annual conference of the ACM Special Interest Group on Data Communication on the applications, technologies, architectures, and protocols for computer communication, pages 632–647, Virtual Event USA. ACM.
Badarla, V. and Murthy, C. S. R. (2011). Learning-TCP: A stochastic approach for efficient update in TCP congestion window in ad hoc wireless networks. Journal of Parallel and Distributed Computing, 71(6):863–878.
Bai, L., Abe, H., and Lee, C. (2020). RNN-based Approach to TCP throughput prediction. In 2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW), pages 391–395, Naha, Japan. IEEE.
Brakmo, L. S., O’Malley, S. W., and 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, page 24–35, New York, NY, USA. Association for Computing Machinery.
Conlin, R., Erickson, K., Abbate, J., and Kolemen, E. (2021). Keras2c: A library for converting Keras neural networks to real-time compatible C. Engineering Applications of Artificial Intelligence, 100:104182. 0000063.
Emara, S., Li, B., and Chen, Y. (2020). Eagle: Refining Congestion Control by Learning from the Experts. In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, pages 676–685, Toronto, ON, Canada. IEEE.
Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4):193–202. 0000061.
Hochreiter, S. and Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8):1735–1780. 0000060 Conference Name: Neural Computation.
ITU, G. C. R. (2023). Global Connectivity Report 2022 - [link] acesso em 13/12/23.
Jay, N., Rotman, N., Godfrey, B., Schapira, M., and Tamar, A. (2019). A deep reinforcement learning perspective on internet congestion control. In Chaudhuri, K. and Salakhutdinov, R., editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 3050–3059. PMLR.
Kazama, R., Abe, H., and Lee, C. (2022). Evaluating TCP throughput predictability from packet traces using recurrent neural network. In 2022 IEEE Symposium on Computers and Communications (ISCC), pages 1–6. 0000057 ISSN: 2642-7389.
Li, W., Zhou, F., Chowdhury, K. R., and Meleis, W. (2019). QTCP: Adaptive Congestion Control with Reinforcement Learning. IEEE Transactions on Network Science and Engineering, 6(3):445–458.
Li, W., Zhou, F., Meleis, W., and Chowdhury, K. (2016). Learning-Based and Data-Driven TCP Design for Memory-Constrained IoT. In 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS), pages 199–205, Washington, DC, USA. IEEE.
Lippmann, R. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine, 4(2):4–22. 0000062 Conference Name: IEEE ASSP Magazine.
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G. (2018). Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering, pages 1–1. 0000065 arXiv:2004.05785 [cs, stat].
Ng, S. W. and Chan, E. (2005). Equation-based TCP-friendly congestion control under lossy environment. Journal of Systems Architecture, 51(9):542–569.
ns3 (2023). A discrete-event network simulator for internet systems - [link] - vistado em 13/12/05.
Ramana, B., Manoj, B., and Murthy, C. (2005). Learning-TCP: a novel learning automata based reliable transport protocol for ad hoc wireless networks. In 2nd International Conference on Broadband Networks, 2005., pages 521–530, Boston, MA. IEEE.
Sutton, R. S. and Barto, A. G. (2018). Reinforcement Learning: An Introduction. The MIT Press, second edition.
Winstein, K. and Balakrishnan, H. (2013). Tcp ex machina: Computer-generated congestion control. In Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM, SIGCOMM ’13, page 123–134, New York, NY, USA. Association for Computing Machinery.
Badarla, V. and Murthy, C. S. R. (2011). Learning-TCP: A stochastic approach for efficient update in TCP congestion window in ad hoc wireless networks. Journal of Parallel and Distributed Computing, 71(6):863–878.
Bai, L., Abe, H., and Lee, C. (2020). RNN-based Approach to TCP throughput prediction. In 2020 Eighth International Symposium on Computing and Networking Workshops (CANDARW), pages 391–395, Naha, Japan. IEEE.
Brakmo, L. S., O’Malley, S. W., and 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, page 24–35, New York, NY, USA. Association for Computing Machinery.
Conlin, R., Erickson, K., Abbate, J., and Kolemen, E. (2021). Keras2c: A library for converting Keras neural networks to real-time compatible C. Engineering Applications of Artificial Intelligence, 100:104182. 0000063.
Emara, S., Li, B., and Chen, Y. (2020). Eagle: Refining Congestion Control by Learning from the Experts. In IEEE INFOCOM 2020 - IEEE Conference on Computer Communications, pages 676–685, Toronto, ON, Canada. IEEE.
Fukushima, K. (1980). Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4):193–202. 0000061.
Hochreiter, S. and Schmidhuber, J. (1997). Long Short-Term Memory. Neural Computation, 9(8):1735–1780. 0000060 Conference Name: Neural Computation.
ITU, G. C. R. (2023). Global Connectivity Report 2022 - [link] acesso em 13/12/23.
Jay, N., Rotman, N., Godfrey, B., Schapira, M., and Tamar, A. (2019). A deep reinforcement learning perspective on internet congestion control. In Chaudhuri, K. and Salakhutdinov, R., editors, Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 3050–3059. PMLR.
Kazama, R., Abe, H., and Lee, C. (2022). Evaluating TCP throughput predictability from packet traces using recurrent neural network. In 2022 IEEE Symposium on Computers and Communications (ISCC), pages 1–6. 0000057 ISSN: 2642-7389.
Li, W., Zhou, F., Chowdhury, K. R., and Meleis, W. (2019). QTCP: Adaptive Congestion Control with Reinforcement Learning. IEEE Transactions on Network Science and Engineering, 6(3):445–458.
Li, W., Zhou, F., Meleis, W., and Chowdhury, K. (2016). Learning-Based and Data-Driven TCP Design for Memory-Constrained IoT. In 2016 International Conference on Distributed Computing in Sensor Systems (DCOSS), pages 199–205, Washington, DC, USA. IEEE.
Lippmann, R. (1987). An introduction to computing with neural nets. IEEE ASSP Magazine, 4(2):4–22. 0000062 Conference Name: IEEE ASSP Magazine.
Lu, J., Liu, A., Dong, F., Gu, F., Gama, J., and Zhang, G. (2018). Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering, pages 1–1. 0000065 arXiv:2004.05785 [cs, stat].
Ng, S. W. and Chan, E. (2005). Equation-based TCP-friendly congestion control under lossy environment. Journal of Systems Architecture, 51(9):542–569.
ns3 (2023). A discrete-event network simulator for internet systems - [link] - vistado em 13/12/05.
Ramana, B., Manoj, B., and Murthy, C. (2005). Learning-TCP: a novel learning automata based reliable transport protocol for ad hoc wireless networks. In 2nd International Conference on Broadband Networks, 2005., pages 521–530, Boston, MA. IEEE.
Sutton, R. S. and Barto, A. G. (2018). Reinforcement Learning: An Introduction. The MIT Press, second edition.
Winstein, K. and Balakrishnan, H. (2013). Tcp ex machina: Computer-generated congestion control. In Proceedings of the ACM SIGCOMM 2013 Conference on SIGCOMM, SIGCOMM ’13, page 123–134, New York, NY, USA. Association for Computing Machinery.
Publicado
20/05/2024
Como Citar
MARCONDES, Cesar Augusto C.; SIVA, Marcelo R. da.
Redes de Aprendizado Profundo para Classificação e Controle de Congestionamento em Redes TCP/IP. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 42. , 2024, Niterói/RJ.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 57-70.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2024.1253.
