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An Optimization Approach to DWDM Network Reconfiguration through Reinforcement Learning

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Abstract

Optical networks must support dynamic reconfiguration to fulfill the requirements of the upcoming generationof data-hungry applications. Reconfiguration can lead to increased network utilization, higher bit rates, increased capacity, and lower QoS violations. With the objective of maximization of utilization under multiple constraints of latency, jitter and transmission quality, this paper proposes a reinforcement-learning-based reconfiguration for dense wavelength division multiplexing (DWDM) Networks. The reconfiguration is addressed both at the local switch and global network level for higher performance gains. A mixed strategy with strategy selection based on reinforcement learning is proposed in this study, as opposed to earlier works that used a single reconfiguration strategy such as wavelength selection, transceiver parameter tuning, route path planning, bandwidth adjustment, etc. A simulation of the proposed solution is performed by integrating MatLab with the OptiSystem Simulator. As compared to existing work, the proposed solution increased network utilization by 4.10% and reduced QoS violations by 2.2 times.

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Data Availability

The dataset produced and analyzed in this study can be obtained from the corresponding author upon reasonable request.

References

  1. Neilson DT, Doerr CR, Marom DM, Ryf R, Earnshaw MP. Wavelength selective switching for optical bandwidth management. Bell Labs Tech J. 2006;11(2):105–28.

    Article  Google Scholar 

  2. P David. Next-generation components for optical access networks. In: Proceedings of optical fiber communication conference and exposition. 2011.

  3. Yuefeng J, Zeyuan Y. Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies. Sci China Inf Sci. 2020;63:6.

    Google Scholar 

  4. Chen C, Seyedi A, Fiorentino M. A comb laser-driven DWDM silicon photonic transmitter based on microring modulators. Opt Express. 2015;23:21541–8.

    Article  Google Scholar 

  5. Lodha L, Agarwal A. Optimization of DWDM system using hybrid optical amplifier with ultra small channel spacing. J Eng Sci. 2020;11:515–9.

    Google Scholar 

  6. Eira A, Santos J, Pedro J, Pires J. Multi-objective design of survivable flexible-grid DWDM networks. J Opt Commun Netw. 2014;6(3):326–39.

    Article  Google Scholar 

  7. Varasteh A, Patri K, Autenrieth A. Towards dynamic network reconfigurations for flexible optical network planning. In: Proceedings of optical fiber communication conference. 2021

  8. Jha R, Llah B. Software Defined Optical Networks (SDON): proposed architecture and comparative analysis. J Eur Opt Soc Rapid Publ. 2019. https://doi.org/10.1186/s41476-019-0105-4.

    Article  Google Scholar 

  9. Boutaba R, Shahriar N, Fathi S. Elastic optical networking for 5G transport. J Netw Syst Manage. 2017. https://doi.org/10.1007/s10922-017-9434-z.

    Article  Google Scholar 

  10. Kadohata A, Hirano A, Inuzuka F, Watanabe A, Ishida O. Wavelength path reconfiguration design in transparent optical WDM networks. IEEE/OSA J Opt Commun Netw. 2013;5:751–61.

    Article  Google Scholar 

  11. Talli G, et al. SDN enabled dynamically reconfigurable high capacity optical access architecture for converged services. J Lightwave Technol. 2017;35(3):550–60.

    Article  Google Scholar 

  12. Urban PJ, et al. High-bit-rate dynamically reconfigurable WDM–TDM access network. J Opt Commun Netw. 2009;1(2):A143–59.

    Article  Google Scholar 

  13. Niu J, Sun Y, Zhang Y, Ji Y. Noise-suppressing channel allocation in dynamic DWDM-QKD networks using LightGBM. Opt Express. 2019;27:31741. https://doi.org/10.1364/OE.27.031741.

    Article  Google Scholar 

  14. Eramo V, Lavacca FG, Catena T, Giorgio F. Reconfiguration of optical-NFV network architectures based on cloud resource allocation and QoS degradation cost-aware prediction techniques. IEEE Access. 2020;8:1–1. https://doi.org/10.1109/ACCESS.2020.3035749.

    Article  Google Scholar 

  15. Hsiu-Sheng L, Po-Chou L. 40 Gb/s DWDM structure with optical phase configuration for long-haul transmission system. Opt Commun. 2016. https://doi.org/10.1515/joc-2016-0019.

    Article  Google Scholar 

  16. Choi Y-K, Hanawa M, Wang X, Park C-S. Upstream transmission of WDM/OCDM-PON in a loop-back configuration with remotely supplied short optical pulses. J Opt Commun Netw. 2013;5:183–9.

    Article  Google Scholar 

  17. Ji Y, Gu R, Yang Z, Li J, Li H, Zhang M. Artificial intelligence-driven autonomous optical networks: 3S architecture and key technologies. Sci China Inf Sci. 2020. https://doi.org/10.1007/s11432-020-2871-2.

    Article  Google Scholar 

  18. T. Enderle et al. Reconfigurable resource allocation in dynamic transport networks. In: Photonic networks; 20th ITG-symposium, 2019. pp. 1–3

  19. Sairam KV, Chandra S, Sai Vamsi P, Sreekantha DK, Annapurna K, Rao S. Survivability reconfigurable techniques in optical network. J Inf Opt Sci. 2019;40(5):1059–67.

    Google Scholar 

  20. Xu T, Jacobsen G, Popov S, Li J, Vanin E, Wang K, Friberg AT, Zhang Y. Chromatic dispersion compensation in coherent transmission system using digital filters. Opt Express. 2010;18:16243–57.

    Article  Google Scholar 

  21. Dar AB, Jha RK. Chromatic dispersion compensation techniques and characterization of fiber Bragg grating for dispersion compensation. Opt Quantum Electron. 2017;49:108.

    Article  Google Scholar 

  22. Pradhan SR, Sahoo SR, Pradhani GR, Panda T. Chromatic dispersion compensation using adaptive fiber bragg grating for high-speed optical communication. ECS Trans. 2022;107:7201.

    Article  Google Scholar 

  23. Zhong Z, Ghobadi M, Khaddaj A, Leach J, Xia Y, Zhang Y. ARROW: restoration-aware traffic engineering. 2021. https://doi.org/10.1145/3452296.3472921.

  24. Li X, Gan C, Gou K, Zhang Y. A novel WDM-MAN enabling cross-regional reconfiguration and comprehensive protection based on tangent-ring. Opt Commun. 2018. https://doi.org/10.1016/j.optcom.2018.08.065.

    Article  Google Scholar 

  25. Klaus-Tycho F, Long L, Manya G. OptFlow: a flow-based abstraction for programmable topologies. In: Proceedings of the symposium on SDN research (SOSR ‘20). Association for Computing Machinery, New York, NY, USA. 2020. pp. 96–102

  26. Morea A, Paparella A. Cost and algorithm complexity of elastic optical networks. Opt Fiber Commun Conf Exhibit. 2016;2016:1–3.

    Google Scholar 

  27. Tarhani M, Sarkar S, Eghbal M, Shadaram M. Resource allocation using SPA based on different cost functions in elastic optical networks. J Comput Commun. 2019;7:14–20.

    Article  Google Scholar 

  28. Poggiolini P, et al. The GN-model of fiber non-linear propagation and its applications. J Lightwave Technol. 2014;32(4):694–721.

    Article  Google Scholar 

  29. Fang Y, Lü Z, Su Z, Wang Y, Zhang T, Zhang Q. Local search based on a new neighborhood for routing and wavelength assignment. In: 2020 IEEE international conference on systems, man, and cybernetics (SMC). 2020. pp. 1123–1128

  30. Simranjit S, Kaler RS. Performance optimization of EDFA–Raman hybrid optical amplifier using genetic algorithm. Opt Laser Technolo. 2015;68:89–95.

    Article  Google Scholar 

  31. https://optiwave.com/optisystem-overview/

  32. Knight S, Nguyen HX, Falkner N, Bowden R, Roughan M. The internet topology zoo. IEEE J Sel Areas Commun. 2011;29(9):1765–75.

    Article  Google Scholar 

  33. Sivanathan A, Sherratt D, Gharakheili HH, Radford A, Wijenayake C, Vishwanath A, Sivaraman V. Characterizing and classifying IoT traffic in smart cities and campuses. In: Proc. IEEE INFOCOM workshops. 2017. pp. 559–565.

  34. Sharma G, Agarwala A, Bhattacharya B. A fast parallel Gauss Jordan algorithm for matrix inversion using CUDA. Comput Struct. 2013;128:31–7.

    Article  Google Scholar 

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Acknowledgements

The research work was made possible with the support of Akash Institute of Engineering and Technology, Bengaluru, Karnataka, India and KLS Gogte institute of Technology, Belagavi, Karnataka, India which provided the necessary facilities.

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Correspondence to Deepthi Prakash.

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Prakash, D., Managuli, M. An Optimization Approach to DWDM Network Reconfiguration through Reinforcement Learning. SN COMPUT. SCI. 5, 1069 (2024). https://doi.org/10.1007/s42979-024-03438-4

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