Machine Learning Assisted Traffic-Aware Approach to Path Assignment in SDM-EONs

  • Ramon A. Oliveira UFPA
  • Denis Rosário UFPA
  • Eduardo Cerqueira UFPA
  • Helder Oliveira UFPA


The introduction of new technologies and applications connected to the Internet has demonstrated the inability of current optical networks to provide resources for next-generation Internet. Although the emergence of elastic optical networks with space-division multiplexing has shown to be a promising solution to deal with the capacity problem, some of the technical requirements for the implementation of these networks remain open challenges. In this sense, this paper proposes MISSION, a Machine Learning assisted, fragmentation, and crosstalk-aware model for path allocation in Space Division Multiplexing Elastic Optical Networks (SDM-EONs). The proposed approach is capable of ordering candidate paths for allocation based on metrics such as crosstalk, fragmentation, and the number of slots. Besides, MISSION shows competitive performance, by keeping a comparatively low blocking probability and fragmentation, even under heavy loads.


Amirabadi, M. (2019). A survey on machine learning for optical communication [machine learning view]. arXiv preprint arXiv:1909.05148.

Beyragh, A. A., Rahbar, A. G., Ghazvini, S.-M. H., and Nickray, M. (2019). If-rsca: intelligent fragmentation-aware method for routing, spectrum and core assignment in space division multiplexing elastic optical networks (sdm-eon). Optical Fiber Technology, 50:284–301.

Cisco, U. (2018). Cisco visual networking index (vni). complete forecast update, 20172022. Accessed: 2020-07-03.

El Naqa, I. and Murphy, M. J. (2015). What is machine learning? In machine learning in radiation oncology, pages 3–11. Springer.

Grinberg, M. (2018). Flask web development: developing web applications with python. ” O’Reilly Media, Inc.”.

Harris, C. R., Millman, K. J., van der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., van Kerkwijk, M. H., Brett, M., Haldane, A., del Río, J. F., Wiebe, M., Peterson, P., Gérard-Marchant, P., Sheppard, K., Reddy, T., Weckesser, W., Abbasi, H., Gohlke, C., and Oliphant, T. E. (2020). Array programming with NumPy. Nature, 585(7825):357–362.

Khan, F. N., Fan, Q., Lu, J., Zhou, G., Lu, C., and Lau, P. T. (2020). Applications of machine learning in optical communications and networks. In Optical Fiber Communication Conference, pages M1G–5. Optical Society of America.

Moura, P. M. and da Fonseca, N. L. S. (2018). Routing, core, modulation level, and spectrum assignment based on image processing algorithms. J. Opt. Commun. Netw., 10(12):947–958.

Moura, P. M. and Drummond, A. C. FlexGridSim: Flexible Grid Optical Network Simulator.

Oliveira, H. M. N. S. and Fonseca, N. L. S. (2019). Multipath routing, spectrum and core allocation in protected sdm elastic optical networks. In 2019 IEEE Global Communications Conference (GLOBECOM), pages 1–6. IEEE.

Paira, S., Halder, J., Chatterjee, M., and Bhattacharya, U. (2020). On energy efficient survivable multipath based approaches in space division multiplexing elastic optical network: Crosstalk-aware and fragmentation-aware. IEEE Access, 8:47344–47356.

pandas development team, T. (2020). pandas-dev/pandas: Pandas.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12:2825–2830.

Rodrigues, E., Figueiredo, G., Santi, J., and Oliveira, H. (2021). Roteamento e alocação de núcleo e espectro com ciência de fragmentação e crosstalk em sdm-eon. In Anais do XXXIX Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pages 546–559, Porto Alegre, RS, Brasil. SBC.

Trindade, S. and da Fonseca, N. L. (2021). Machine learning for spectrum defragmentation in space-division multiplexing elastic optical networks. IEEE Network, 35(1):326– 332.

Trindade, S. and da Fonseca, N. L. S. (2019). Proactive fragmentation-aware routing, modulation format, core, and spectrum allocation in eon-sdm. In ICC 2019 2019 IEEE International Conference on Communications (ICC), pages 1–6.

Van Rossum, G. (2020). The Python Library Reference, release 3.8.2. Python Software Foundation.

Yang, S., Yang, L., Luo, F., Wang, X., Li, B., Du, Y., and Liu, D. (2021). Multi-channel multi-task optical performance monitoring based multi-input multi-output deep learning and transfer learning for sdm. Optics Communications, 495:127110.

Yao, Q., Yang, H., Yu, B., Yu, A., Bai, W., and Zhang, J. (2018a). Spectrum optimization for resource reservation based on transductive transfer learning in space division multiplexing elastic optical networks. In 2018 European Conference on Optical Communication (ECOC), pages 1–3.

Yao, Q., Yang, H., Zhu, R., Yu, A., Bai, W., Tan, Y., Zhang, J., and Xiao, H. (2018b). Core, mode, and spectrum assignment based on machine learning in space division multiplexing elastic optical networks. IEEE Access, 6:15898–15907.

Yousefi, F., Ghaffarpour Rahbar, A., and Ghadesi, A. (2020). Fragmentation and time aware algorithms in spectrum and spatial assignment for space division multiplexed elastic optical networks (sdm-eon). Computer Networks, 174:107232.

Yousefi, F. and Rahbar, A. G. (2020). Novel crosstalk, fragmentation-aware algorithms in space division multiplexed-elastic optical networks (sdm-eon) with considering physical layer security. Optical Switching and Networking, 37:100566.

Zhao, Y., Hu, L., Zhu, R., Yu, X., Wang, X., and Zhang, J. (2018). Crosstalk-aware spectrum defragmentation based on spectrum compactness in space division multiplexing enabled elastic optical networks with multicore fiber. IEEE Access, 6:15346–15355.

Zhu, R., Samuel, A., Wang, P., Li, S., Oun, B. K., Li, L., Lv, P., Xu, M., and Yu, S. (2021). Protected resource allocation in space division multiplexing-elastic optical networks with fluctuating traffic. Journal of Network and Computer Applications, 174:102887.

Ítalo Brasileiro, Costa, L., and Drummond, A. (2020). A survey on challenges of spatial division multiplexing enabled elastic optical networks. Optical Switching and Networking, 38:100584.
Como Citar

Selecione um Formato
OLIVEIRA, Ramon A.; ROSÁRIO, Denis; CERQUEIRA, Eduardo; OLIVEIRA, Helder. Machine Learning Assisted Traffic-Aware Approach to Path Assignment in SDM-EONs. In: SIMPÓSIO BRASILEIRO DE REDES DE COMPUTADORES E SISTEMAS DISTRIBUÍDOS (SBRC), 40. , 2022, Fortaleza. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 29-42. ISSN 2177-9384. DOI:

Artigos mais lidos do(s) mesmo(s) autor(es)

1 2 3 > >>