Classificador baseado em Aprendizado Profundo para identificação de estratégias de alocação de espectro em Redes Ópticas Elásticas
Resumo
As redes ópticas elásticas proporcionam uma flexibilidade e escalabilidade na alocação de espectro superior às tradicionais redes baseadas em multiplexação por divisão de comprimento de onda, sendo adequadas para suportar a crescente demanda do tráfego da internet. Atualmente, os algoritmos de roteamento e alocação de espectro dessas redes utilizam estratégias fixas. A tendência natural de evolução desses algoritmos é a utilização de estratégias adaptativas, de acordo com o comportamento da rede. Para isso, alguns passos são necessários. O primeiro deles é a criação de um classificador capaz de identificar as estratégias de alocação de espectro que estão sendo utilizadas, foco desse trabalho. O classificador baseado em aprendizado profundo obteve acurácia de 98%. Os resultados obtidos abrem caminho para uma nova classe de algoritmos para redes ópticas elásticas, nos quais a tomada de decisão pode ser feita adaptativamente.
Palavras-chave:
Redes Ópticas Elásticas, Roteamento e Alocação de Espectro, Aprendizagem de Máquina, Aprendizagem Profunda
Referências
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Muhammad, A., Zervas, G., and Forchheimer, R. (2015). Resource allocation for space-division multiplexing: Optical white box versus optical black box networking. Journal of Lightwave Technology, 33(23):4928–4941.
Musumeci, F., Rottondi, C., Nag, A., Macaluso, I., Zibar, D., Ruffini, M., and Tornatore, M. (2018). An overview on application of machine learning techniques in optical networks. IEEE Communications Surveys & Tutorials, 21(2):1383–1408.
Proietti, R., Chen, X., Castro, A., Liu, G., Lu, H., Zhang, K., Guo, J., Zhu, Z., Velasco, L., and Yoo, S. J. B. (2018). Experimental demonstration of cognitive provisioning and alien wavelength monitoring in multi-domain EON. In 2018 Optical Fiber Communications Conference and Exposition (OFC), pages 1–3.
Rosa, A., Cavdar, C., Carvalho, S., Costa, J., and Wosinska, L. (2012). Spectrum allocation policy modeling for elastic optical networks. In High Capacity Optical Networks and Emerging/Enabling Technologies, pages 242–246.
Tessinari, R. S., Colle, D., and Garcia, A. S. (2018). Cognitive zone-based spectrum assignment algorithm for elastic optical networks. In 2018 International Conference on Optical Network Design and Modeling (ONDM), pages 112–117.
Tode, H. and Hirota, Y. (2016). Routing, Spectrum and Core Assignment on SDM Optical Networks (Invited). 9(1):Tu2H.1.
Vela, A. P., Ruiz, M., Fresi, F., Sambo, N., Cugini, F., Meloni, G., Potı̀, L., Velasco, L., and Castoldi, P. (2017). Ber degradation detection and failure identification in elastic optical networks. Journal of Lightwave Technology, 35(21):4595–4604.
Velasco, L., Vela, A. P., Morales, F., and Ruiz, M. (2017). Designing, operating, and reoptimizing elastic optical networks. Journal of Lightwave Technology, 35(3):513–526.
Waldman, H. (2018). The impending optical network capacity crunch. In 2018 SBFoton International Optics and Photonics Conference (SBFoton IOPC), pages 1–4.
Walkowiak, K., Klinkowski, M., and Lechowicz, P. (2018). Dynamic routing in spectrally spatially flexible optical networks with back-to-back regeneration. IEEE/OSA Journal of Optical Communications and Networking, 10(5):523–534.
Wang, R. and Mukherjee, B. (2012). Spectrum management in heterogeneous bandwidth networks. In 2012 IEEE Global Communications Conference (GLOBECOM), pages 2907– 2911.
Yan, S., N Khan, K., Mavromatis, A., Gkounis, D., Fan, Q., Ntavou, F., Nikolovgenis, K., Guo, C., Meng, F., Hugues-Salas, E., Lu, C., Pak Tau Lau, A., Nejabati, R., and Simeonidou, D. (2017). Field trial of machine-learning-assisted and sdn-based optical network planning with network-scale monitoring database. page TH.PDP.B4.
Barletta, L., Giusti, A., Rottondi, C., and Tornatore, M. (2017). QoT estimation for unestablished lighpaths using machine learning. In 2017 Optical Fiber Communications Conference and Exhibition (OFC), pages 1–3.
Boutaba, R., Salahuddin, M. A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F., and Caicedo, O. M. (2018). A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. Journal of Internet Services and Applications, 9(1):16.
Chatterjee, B., Sarma, N., and Oki, E. (2015). Routing and spectrum allocation in elastic optical networks: A tutorial. IEEE Communications Surveys Tutorials, 17(3):1776–1800.
Chatterjee, B. C., Fadini, W., and Oki, E. (2016). A spectrum allocation scheme based on first–last-exact fit policy for elastic optical networks. Journal of Network and Computer Applications, 68:164 – 172.
Chen, X., Guo, J., Zhu, Z., Proietti, R., Castro, A., and Yoo, S. J. B. (2018). Deep-RMSA: A deep-reinforcement-learning routing, modulation and spectrum assignment agent for elastic optical networks. In 2018 Optical Fiber Communications Conference and Exposition (OFC), pages 1–3.
Costa, L. and Drummond, A. (2017). Novo Esquema para Provisão de Modulação Adaptativa em Redes ópticas Elásticas. In SBRC 2017, Belém, Pará.
Costa, L. R. and Drummond, A. C. (2019). ONS - optical network simulator. https://ons-simulator.com.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep learning. MIT press.
Jia, W., Xu, Z., Ding, Z., and Wang, K. (2016). An efficient routing and spectrum assignment algorithm using prediction for elastic optical networks. In 2016 International Conference on Information System and Artificial Intelligence (ISAI), pages 89–93.
Keras (2019). Keras: The python deep learning library. https://keras.io/. Accessed: 2019-10-04.
Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization.
Lacerda, J., Fontinele, A., Campelo, D., and Soares, A. (2017). Proteção dedicada para redes Ópticas elásticas considerando efeitos de camada fı́sica. In SBRC 2017, Belém, Pará.
Leiva, A., Pavez, N., Beghelli, A., and Olivares, R. (2014). A joint rsa algorithm for dynamic flexible optical networking. In 2014 IEEE Latin-America Conference on Communications (LATINCOM), pages 1–6.
Mata, J., de Miguel, I., Durán, R. J., Merayo, N., Singh, S. K., Jukan, A., and Chamania, M. (2018). Artificial intelligence (AI) methods in optical networks: A comprehensive survey. Optical Switching and Networking, 28:43 – 57.
Morales, F., Ruiz, M., Gifre, L., Contreras, L. M., Lopez, V., and Velasco, L. (2017). Virtual network topology adaptability based on data analytics for traffic prediction. IEEE/OSA Journal of Optical Communications and Networking, 9(1):A35–A45.
Muhammad, A., Zervas, G., and Forchheimer, R. (2015). Resource allocation for space-division multiplexing: Optical white box versus optical black box networking. Journal of Lightwave Technology, 33(23):4928–4941.
Musumeci, F., Rottondi, C., Nag, A., Macaluso, I., Zibar, D., Ruffini, M., and Tornatore, M. (2018). An overview on application of machine learning techniques in optical networks. IEEE Communications Surveys & Tutorials, 21(2):1383–1408.
Proietti, R., Chen, X., Castro, A., Liu, G., Lu, H., Zhang, K., Guo, J., Zhu, Z., Velasco, L., and Yoo, S. J. B. (2018). Experimental demonstration of cognitive provisioning and alien wavelength monitoring in multi-domain EON. In 2018 Optical Fiber Communications Conference and Exposition (OFC), pages 1–3.
Rosa, A., Cavdar, C., Carvalho, S., Costa, J., and Wosinska, L. (2012). Spectrum allocation policy modeling for elastic optical networks. In High Capacity Optical Networks and Emerging/Enabling Technologies, pages 242–246.
Tessinari, R. S., Colle, D., and Garcia, A. S. (2018). Cognitive zone-based spectrum assignment algorithm for elastic optical networks. In 2018 International Conference on Optical Network Design and Modeling (ONDM), pages 112–117.
Tode, H. and Hirota, Y. (2016). Routing, Spectrum and Core Assignment on SDM Optical Networks (Invited). 9(1):Tu2H.1.
Vela, A. P., Ruiz, M., Fresi, F., Sambo, N., Cugini, F., Meloni, G., Potı̀, L., Velasco, L., and Castoldi, P. (2017). Ber degradation detection and failure identification in elastic optical networks. Journal of Lightwave Technology, 35(21):4595–4604.
Velasco, L., Vela, A. P., Morales, F., and Ruiz, M. (2017). Designing, operating, and reoptimizing elastic optical networks. Journal of Lightwave Technology, 35(3):513–526.
Waldman, H. (2018). The impending optical network capacity crunch. In 2018 SBFoton International Optics and Photonics Conference (SBFoton IOPC), pages 1–4.
Walkowiak, K., Klinkowski, M., and Lechowicz, P. (2018). Dynamic routing in spectrally spatially flexible optical networks with back-to-back regeneration. IEEE/OSA Journal of Optical Communications and Networking, 10(5):523–534.
Wang, R. and Mukherjee, B. (2012). Spectrum management in heterogeneous bandwidth networks. In 2012 IEEE Global Communications Conference (GLOBECOM), pages 2907– 2911.
Yan, S., N Khan, K., Mavromatis, A., Gkounis, D., Fan, Q., Ntavou, F., Nikolovgenis, K., Guo, C., Meng, F., Hugues-Salas, E., Lu, C., Pak Tau Lau, A., Nejabati, R., and Simeonidou, D. (2017). Field trial of machine-learning-assisted and sdn-based optical network planning with network-scale monitoring database. page TH.PDP.B4.
Publicado
07/12/2020
Como Citar
SILVA, Guilherme Enéas Vaz; COSTA, Lucas Rodrigues; DRUMMOND, André Costa.
Classificador baseado em Aprendizado Profundo para identificação de estratégias de alocação de espectro 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. 337-350.
ISSN 2177-9384.
DOI: https://doi.org/10.5753/sbrc.2020.12293.
