Alocação de Banda de Guarda Adaptativa Utilizando Redes Neurais Multi Layer Perceptron em Redes Ópticas Elásticas
Resumo
Routing, Modulation Level and Spectrum Assignment (RMLSA) é um dos principais problemas estudados nas redes ópticas elásticas. Este trabalho concentra-se no estudo da seleção da banda de guarda, um ou mais slots livres entre os circuitos, que é usada nas soluções do problema RMLSA. Neste contexto, uma nova abordagem, chamada de GUARDIAN, que usa uma rede neural multi layer perceptron para selecionar a banda de guarda de forma adaptativa é proposta. O desempenho da proposta é comparado com outras propostas adaptativas: AGBA e GBUN. A proposta alcança uma redução na probabilidade de bloqueio de banda de pelo menos 54,01% em relação ao AGBA e 51,26% em relação ao GBUN.
Palavras-chave:
RMLSA, Redes Ópticas Elásticas, Banda de Guarda, Adaptativa, Rede Neural, Multi Layer Perceptron
Referências
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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.
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Wu, J., Ning, Z., and Guo, L. (2017). Energy-efficient survivable grooming in software-defined 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.
Beyranvand, H. and Salehi, J. (2013). A quality-of-transmission aware dynamic routing and spectrum assignment scheme for future elastic optical networks. Journal of Lightwave Technology, 31(18):3043–3054.
Borges, F. A., Fernandes, R. A., Silva, I. N., and Silva, C. B. (2016). Feature extraction and power quality disturbances classification using smart meters signals. IEEE Transactions on Industrial Informatics, 12(2):824–833.
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.
Chen, C., Ju, M., Xiao, S., Zhou, F., and Yang, X. (2017). Minimizing total blocking by setting optimal guard band in nonlinear elastic optical networks. In 2017 19th International Conference on Transparent Optical Networks (ICTON), pages 1–4.
Chen, X., Zhong, Y., and Jukan, A. (2013). Multipath routing in elastic optical networks with distance-adaptive modulation formats. In 2013 IEEE International Conference on Communications (ICC), pages 3915–3920.
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 fiber-optic 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.
Monteiro, N., Fontinele, A., Santos, I. G., Oliveira, A., Campelo, D., and Soares, A. (2019). Novo algoritmo para provisão de banda de guarda adaptativa em redes Ópticas elásticas. In Anais do XXXVII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 307–320, Gramado, RS, Brasil. SBC.
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.
Takeshita, H., Noguchi, H., Abe, J., Fujisawa, S., and Tajima, A. (2016). Adaptive guard-band assignment with adaptive spectral profile equalizer to improve spectral usage of impairment-aware elastic optical network. In ECOC 2016; 42nd European Conference on Optical Communication, 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 software-defined 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.
Publicado
07/12/2020
Como Citar
MONTEIRO, Neclyeux; JUNIOR, Wilson; FONTINELE, Alexandre; CAMPELO, Divanilson; PAIVA, Anselmo; RABÊLO, Ricardo; SOARES, André.
Alocação de Banda de Guarda Adaptativa Utilizando Redes Neurais Multi Layer Perceptron em Redes Ópticas Elásticas. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 38. , 2020, Rio de Janeiro.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2020
.
p. 770-783.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12324.
