IRON: uma abordagem inteligente para roteamento em redes ópticas elásticas
Resumo
As redes ópticas elásticas compõem uma infraestrutura de rede capaz de suportar a grande demanda de tráfego de dados da Internet. Um dos principais problemas que devem ser solucionados para garantir o bom funcionamento deste tipo de rede é o chamado Routing, Modulation Level and Spectrum Assignment (RMLSA). Este artigo foca em uma nova solução para o problema de roteamento. A nova solução utiliza uma rede neural Multi Layer Perceptron e é chamada de Intelligent Routing for Optical Networks (IRON). A solução é comparada com o algoritmo Complete Sharing em dois cenários diferentes utilizando as topologias Cost239 e Pan-European. O algoritmo IRON obteve uma redução na probabilidade de bloqueio de banda de aproximadamente 28,59% e 21,83% em relação ao Complete Sharing nas topologias Cost239 e Pan-European respectivamente.
Referências
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016). Tensorflow: a system for large-scale machine learning. In OSDI, volume 16, pages 265–283.
Chatterjee, B. C., Sarma, N., and Oki, E. (2015). Routing and spectrum allocation in elastic optical networks: A tutorial. IEEE Communications Surveys & Tutorials, 17(3):1776–1800.
Chollet, F. et al. (2015). Keras: Deep learning library for theano and tensorflow. URL: https://keras.io/k, 7(8).
Christodoulopoulos, K., Tomkos, I., and Varvarigos, E. (2011). Elastic bandwidth allocation in flexible OFDM-based optical networks. Journal of Lightwave Technology, 29(9):1354–1366.
Fontinele, A., Santos, I., Neto, J. N., Campelo, D. R., and Soares, A. (2017). An efficient IA-RMLSA algorithm for transparent elastic optical networks. Computer Networks, 118(Supplement C):1 – 14.
Gardner, M.W. and Dorling, S. (1998). Artificial neural networks (the multilayer perceptron)— a review of applications in the atmospheric sciences. Atmospheric environment, 32(14-15):2627–2636.
Hawkins, D. M. (2004). The problem of overfitting. Journal of chemical information and computer sciences, 44(1):1–12.
Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. In Neural networks for perception, pages 65–93. Elsevier.
Ives, D. J., Bayvel, P., and Savory, S. J. (2014). Physical layer transmitter and routing optimization to maximize the traffic throughput of a nonlinear optical mesh network. In 2014 International Conference on Optical Network Design and Modeling, pages 168–173.
Jinno, M., Takara, H., Kozicki, B., Tsukishima, Y., Sone, Y., and Matsuoka, S. (2009). Spectrum-efficient and scalable elastic optical path network: architecture, benefits, and enabling technologies. IEEE Communications Magazine, 47(11):66–73.
Johannisson, P. and Agrell, E. (2014). Modeling of nonlinear signal distortion in fiberoptic networks. Journal of Lightwave Technology, 32(23):4544–4552.
Khokhar, S., Zin, A. A. B. M., Mokhtar, A. S. B., and Pesaran, M. (2015). A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renewable and Sustainable Energy Reviews, 51:1650–1663.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436.
Levesque, M. and Elbiaze, H. (2009). Graphical probabilistic routing model for obs networks with realistic traffic scenario. In GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, pages 1–6.
Mohan, N., Soman, K., and Vinayakumar, R. (2017). Deep power: Deep learning architectures for power quality disturbances classification. In 2017 International Conference on Technological Advancements in Power and Energy (TAP Energy), pages 1–6. IEEE.
Rodrigues, W. L., Sousa Monteiro, N., Silva Borges, F. A., de Andrade Lira Rabelo, R., and Branco Soares, A. C. (2020). An adaptive guard band selection based on convolutional neural network. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2814–2821.
Saradhi, C. V. and Subramaniam, S. (2009). Physical layer impairment aware routing (PLIAR) in WDM optical networks: Issues and challenges. Commun. Surveys Tuts., 11(4):109–130.
Sokolova, M. and Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4):427 – 437.
Trindade, S. and da Fonseca, N. L. S. (2021). Machine learning for spectrum defragmentation in space-division multiplexing elastic optical networks. IEEE Network, 35(1):326–332.
Troia, S., Rodriguez, A., Martín, I., Hernández, J. A., De Dios, O. G., Alvizu, R., Musumeci, F., and Maier, G. (2018). Machine-learning-assisted routing in sdn-based optical networks. In 2018 European Conference on Optical Communication (ECOC), pages 1–3.
Wang, R. and Mukherjee, B. (2014). Spectrum management in heterogeneous bandwidth optical networks. Optical Switching and Networking, 11, Part A:83 – 91.
Wang, S. and Chen, H. (2019). A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Applied Energy, 235:1126 – 1140.
Wen Jin, Zhao Jia Li, Luo Si Wei, and Han Zhen (2000). The improvements of bp neural network learning algorithm. In WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000, volume 3, pages 1647–1649 vol.3.
Wu, J., Ning, Z., and Guo, L. (2017). Energy-efficient survivable grooming in softwaredefined elastic optical networks. IEEE Access, 5:6454–6463.
Yan, L., Agrell, E., Wymeersch, H., Johannisson, P., Di Taranto, R., and Brandt-Pearce, M. (2015). Link-level resource allocation for flexible-grid nonlinear fiber-optic communication systems. IEEE Photonics Technology Letters, 27(12):1250–1253.
Zhao, J., Wymeersch, H., and Agrell, E. (2015). Nonlinear impairment aware resource allocation in elastic optical networks. In 2015 Optical Fiber Communications Conference and Exhibition (OFC), pages 1–3.
Zhong, Z., Hua, N., Yuan, Z., Li, Y., and Zheng, X. (2019). Routing without routing algorithms: An ai-based routing paradigm for multi-domain optical networks. In 2019 Optical Fiber Communications Conference and Exhibition (OFC), pages 1–3.
Chatterjee, B. C., Sarma, N., and Oki, E. (2015). Routing and spectrum allocation in elastic optical networks: A tutorial. IEEE Communications Surveys & Tutorials, 17(3):1776–1800.
Chollet, F. et al. (2015). Keras: Deep learning library for theano and tensorflow. URL: https://keras.io/k, 7(8).
Christodoulopoulos, K., Tomkos, I., and Varvarigos, E. (2011). Elastic bandwidth allocation in flexible OFDM-based optical networks. Journal of Lightwave Technology, 29(9):1354–1366.
Fontinele, A., Santos, I., Neto, J. N., Campelo, D. R., and Soares, A. (2017). An efficient IA-RMLSA algorithm for transparent elastic optical networks. Computer Networks, 118(Supplement C):1 – 14.
Gardner, M.W. and Dorling, S. (1998). Artificial neural networks (the multilayer perceptron)— a review of applications in the atmospheric sciences. Atmospheric environment, 32(14-15):2627–2636.
Hawkins, D. M. (2004). The problem of overfitting. Journal of chemical information and computer sciences, 44(1):1–12.
Hecht-Nielsen, R. (1992). Theory of the backpropagation neural network. In Neural networks for perception, pages 65–93. Elsevier.
Ives, D. J., Bayvel, P., and Savory, S. J. (2014). Physical layer transmitter and routing optimization to maximize the traffic throughput of a nonlinear optical mesh network. In 2014 International Conference on Optical Network Design and Modeling, pages 168–173.
Jinno, M., Takara, H., Kozicki, B., Tsukishima, Y., Sone, Y., and Matsuoka, S. (2009). Spectrum-efficient and scalable elastic optical path network: architecture, benefits, and enabling technologies. IEEE Communications Magazine, 47(11):66–73.
Johannisson, P. and Agrell, E. (2014). Modeling of nonlinear signal distortion in fiberoptic networks. Journal of Lightwave Technology, 32(23):4544–4552.
Khokhar, S., Zin, A. A. B. M., Mokhtar, A. S. B., and Pesaran, M. (2015). A comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances. Renewable and Sustainable Energy Reviews, 51:1650–1663.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
LeCun, Y., Bengio, Y., and Hinton, G. (2015). Deep learning. nature, 521(7553):436.
Levesque, M. and Elbiaze, H. (2009). Graphical probabilistic routing model for obs networks with realistic traffic scenario. In GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, pages 1–6.
Mohan, N., Soman, K., and Vinayakumar, R. (2017). Deep power: Deep learning architectures for power quality disturbances classification. In 2017 International Conference on Technological Advancements in Power and Energy (TAP Energy), pages 1–6. IEEE.
Rodrigues, W. L., Sousa Monteiro, N., Silva Borges, F. A., de Andrade Lira Rabelo, R., and Branco Soares, A. C. (2020). An adaptive guard band selection based on convolutional neural network. In 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pages 2814–2821.
Saradhi, C. V. and Subramaniam, S. (2009). Physical layer impairment aware routing (PLIAR) in WDM optical networks: Issues and challenges. Commun. Surveys Tuts., 11(4):109–130.
Sokolova, M. and Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4):427 – 437.
Trindade, S. and da Fonseca, N. L. S. (2021). Machine learning for spectrum defragmentation in space-division multiplexing elastic optical networks. IEEE Network, 35(1):326–332.
Troia, S., Rodriguez, A., Martín, I., Hernández, J. A., De Dios, O. G., Alvizu, R., Musumeci, F., and Maier, G. (2018). Machine-learning-assisted routing in sdn-based optical networks. In 2018 European Conference on Optical Communication (ECOC), pages 1–3.
Wang, R. and Mukherjee, B. (2014). Spectrum management in heterogeneous bandwidth optical networks. Optical Switching and Networking, 11, Part A:83 – 91.
Wang, S. and Chen, H. (2019). A novel deep learning method for the classification of power quality disturbances using deep convolutional neural network. Applied Energy, 235:1126 – 1140.
Wen Jin, Zhao Jia Li, Luo Si Wei, and Han Zhen (2000). The improvements of bp neural network learning algorithm. In WCC 2000 - ICSP 2000. 2000 5th International Conference on Signal Processing Proceedings. 16th World Computer Congress 2000, volume 3, pages 1647–1649 vol.3.
Wu, J., Ning, Z., and Guo, L. (2017). Energy-efficient survivable grooming in softwaredefined elastic optical networks. IEEE Access, 5:6454–6463.
Yan, L., Agrell, E., Wymeersch, H., Johannisson, P., Di Taranto, R., and Brandt-Pearce, M. (2015). Link-level resource allocation for flexible-grid nonlinear fiber-optic communication systems. IEEE Photonics Technology Letters, 27(12):1250–1253.
Zhao, J., Wymeersch, H., and Agrell, E. (2015). Nonlinear impairment aware resource allocation in elastic optical networks. In 2015 Optical Fiber Communications Conference and Exhibition (OFC), pages 1–3.
Zhong, Z., Hua, N., Yuan, Z., Li, Y., and Zheng, X. (2019). Routing without routing algorithms: An ai-based routing paradigm for multi-domain optical networks. In 2019 Optical Fiber Communications Conference and Exhibition (OFC), pages 1–3.
Publicado
16/08/2021
Como Citar
MONTEIRO, Neclyeux; SOARES, André.
IRON: uma abordagem inteligente para roteamento em redes ópticas elásticas. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 39. , 2021, Uberlândia.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 308-321.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2021.16729.
