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Capacity, resilience and virtual embedding in elastic optical networks planning with adopted machine learning

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Abstract

This work presents supervised machine learning techniques for the problem of virtualization design with protection over elastic optical networks (EONs) for predicting the total number of used spectrum slots to support all traffic demands. It considers virtual optical networks (VONs) subject to protection and proposes learning techniques to solve the link capacity problem of EONs with virtualization faster than traditional integer linear programming (ILP) formulations, but keeping the finds near to the optimal ones. The performance of the models were evaluated using statistical metrics, along with the time for training and performing inferences, both using quantitative and qualitative analysis. They showed that the proposed method is effective for predicting the number of required slots (bandwidth) on physical substrate subject to several VONs.

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References

  • Assis, K., et al.: Network virtualization over elastic optical networks with different protection schemes. IEEE/OSA J. Opt. Commun. Netw. 8(4), 272–281 (2016)

    Article  Google Scholar 

  • Assis, K.D.R., Almeida, R.C.D.A., Dos Santos, A.F., Dias, L.P., Giozza, W.F., De Sousa, R.T., Jaumard, B., Nejabati, R., Simeonidou, D.: Channel-based rsa approaches for qos protection of slices over elastic optical networks. IEEE Access 10, 20714–20726 (2022)

    Article  Google Scholar 

  • Assis, K.D.R., Almeida, R.C., Dias, L.P., Waldman, H.: Squeezed protection in elastic optical networks subject to multiple link failures. IEEE Trans. Netw. Serv. Manage. 18(3), 2612–2626 (2021)

    Article  Google Scholar 

  • Assis, K., Almeida, R., Jr., Reed, M., Santos, A., Dinarte, H., Chaves, D., Li, H., Yan, S., Nejabati, R., Simeonidou, D.: Protection by diversity in elastic optical networks subject to single link failure. Opt. Fiber Technol. 75, 103208 (2023)

    Article  Google Scholar 

  • Bui, M., Jaumard, B., Cavdar, C., Mukherjee, B.: Design of a survivable vpn topology over a service provider network. In: 2013 9th International Conference on the Design of Reliable Communication Networks (DRCN), pp. 71–78. IEEE (2013)

  • Chatterjee, B., Oki, E.: Elastic Optical Networks: Fundamentals. Control, and Management. CRC Press, Design (2020)

    Book  Google Scholar 

  • Davalos, E.J., Baran, B.: A survey on algorithmic aspects of virtual optical network embedding for cloud networks. IEEE 6, 20893–20906 (2018)

    Google Scholar 

  • Dias, L., et al.: Channel-based rsa approach for virtualization and qos-aware protection in optical networks. In: IEEE International Conference on Communications (ICC 2021), pp. 1–6. IEEE (2021)

  • Hai, D.T.: On the spectrum-efficiency of qos-aware protection in elastic optical networks. Optik 202, 163563 (2020)

    Article  ADS  Google Scholar 

  • Hammad, A., et al.: Novel approaches for composition of online virtual optical networks utilizing o-ofdm technology. In: 39th European Conference and Exhibition on Optical Communication (ECOC 2013), pp. 1–3. IET (2013)

  • Jinno, M., et al.: Spectrum-efficient and scalable elastic optical path network: Architecture, benefits, and enabling technologies. IEEE Commun. Mag. 47(11), 66–73 (2009)

    Article  Google Scholar 

  • Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  • Klinkowski, M.: A genetic algorithm for solving rsa problem in elastic optical networks with dedicated path protection. In: International joint conference CISIS’12-ICEUTE ‘12-SOCO’ 12 Special Sessions, pp. 167–176. Springer (2013)

  • Klinkowski, M., Walkowiak, K.: Routing and spectrum assignment in spectrum sliced elastic optical path network. IEEE Commun. Lett. 15(8), 884–886 (2011)

    Article  Google Scholar 

  • Moura, T., Vieira, R., Almeida, R., Giozza, W., Rodrigues, J., Assis, K.D.R.: Planejamento de redes Ópticas elásticas por meio da solução do max-rmsa. In: Anais do XXXVIII Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos, pp. 868–881. SBC (2020)

  • Pióro, M., Medhi, D.: Routing, flow, and capacity design in communication and computer networks. Elsevier (2004)

  • Renjith, P., Sujatha, G., Vinoth, M., Vignesh, G., Prabhu, M.R., Mouleswararao, B.: Deep reinforcement learning for comprehensive route optimization in elastic optical networks using generative strategies. Opt. Quant. Electron. 55(13), 1197 (2023)

    Article  Google Scholar 

  • Roeder, L.: Netron - a viewer for neural network, deep learning and machine learning models (2017). https://github.com/lutzroeder/netron

  • Shahriar, N.: Survivable virtual network embedding in transport networks (2020). http://hdl.handle.net/10012/16085

  • Sone, Y., et al.: Bandwidth squeezed restoration in spectrum-sliced elastic optical path networks (slice). IEEE/OSA J. Opt. Commun. Netw. 3(3), 223–233 (2011)

    Article  Google Scholar 

  • Velasco, L., et al.: Modeling the routing and spectrum allocation problem for flexgrid optical networks. Photon Netw. Commun. 24(3), 177–186 (2012)

    Article  Google Scholar 

  • Wang, L.: Elastic optical network routing analysis using intelligent machine learning architecture with security analysis. Opt. Quant. Electron. 55(14), 1238 (2023)

    Article  Google Scholar 

  • Wang, Y., Cao, X., Pan, Y.: A study of the routing and spectrum allocation in spectrum-sliced elastic optical path networks. In: IEEE International Conference on Computer Communications (INFOCOM), pp. 1503–1511 (2011)

  • Zhang, Y., Xin, J., Li, X., Huang, S.: Overview on routing and resource allocation based machine learning in optical networks. Opt. Fiber Technol. 60, 102355 (2020)

    Article  Google Scholar 

  • Zhang, L., Li, X., Tang, Y., Xin, J., Huang, S.: A survey on qot prediction using machine learning in optical networks. Opt. Fiber Technol. 68, 102804 (2022)

    Article  Google Scholar 

Download references

Acknowledgements

This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. The authors thank to CNPq for scholarships and grants, and to UFBA for its educational support.

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The authors have not disclosed any funding.

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Authors and Affiliations

  1. Department of Electrical Engineering, Federal University of Bahia, Salvador, Bahia, Brazil

    Talison Augusto Correia de Melo & Karcius Day Rosário Assis

  2. Center of Science and Technology in Energy and Sustainability, Federal University of Reconcavo of Bahia. Postgraduate Program in Computer Science, State University of Feira de Santana, Feira de Santana, Bahia, Brazil

    Alex Ferreira dos Santos

  3. Photonics Group, Electronics and Systems Department, Technology Center and Geosciences, Federal University of Pernambuco, Recife, Pernambuco, Brazil

    Raul Camelo de Andrade Almeida Jr.

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  1. Talison Augusto Correia de Melo
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  2. Alex Ferreira dos Santos
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  3. Raul Camelo de Andrade Almeida Jr.
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  4. Karcius Day Rosário Assis
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Correspondence to Karcius Day Rosário Assis.

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de Melo, T.A.C., dos Santos, A.F., de Andrade Almeida, R.C. et al. Capacity, resilience and virtual embedding in elastic optical networks planning with adopted machine learning. Opt Quant Electron 56, 1075 (2024). https://doi.org/10.1007/s11082-024-07016-z

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