Abstract
Over the past few decades, the demand for the capacity and reliability of optical networks has continued to grow. In the meantime, optical networks with larger knowledge scales have become sources of numerous heterogeneous data. In order to handle these new challenges, many issues need to be resolved, among which the low-margin optical networks design, power optimization, routing and wavelength assignment (RWA), failure management are quite important. However, the use of traditional algorithms in the above four applications shows some shortcomings. Fortunately, artificial intelligence (AI), especially machine learning (ML), is regarded as one of the most promising methods to overcome these shortcomings. In this study, we review the applications of ML methods in solving these four issues. Although many ML-based researches have emerged, the applications of ML techniques in optical networks still face challenges. Therefore, we also discuss some possible future directions of investigating ML-based approaches in optical networks.
Similar content being viewed by others
References
Gupta A, Jha R K. A survey of 5G network: architecture and emerging technologies. IEEE Access, 2015, 3: 1206–1232
Gerstel O, Jinno M, Lord A, et al. Elastic optical networking: a new dawn for the optical layer? IEEE Commun Mag, 2012, 50: 12–20
Pointurier Y. Design of low-margin optical networks. J Opt Commun Netw, 2017, 9: 9
Perin J K, Roberts I, Kahn J M. Improving the capacity of terrestrial and submarine systems via channel power optimization. In: Proceedings of International Conference on Transparent Optical Networks, 2018. 1–4
Martin I, Hernandez J A, Troia S, et al. Is machine learning suitable for solving RWA problems in optical networks? In: Proceedings of European Conference on Optical Communication, 2018. 1–3
Musumeci F, Rottondi C, Corani G, et al. A tutorial on machine learning for failure management in optical networks. J Lightw Technol, 2019, 37: 4125–4139
Rafique D, Velasco L. Machine learning for network automation: overview, architecture, and applications. J Opt Commun Netw, 2018, 10: 126
Cote D. Using machine learning in communication networks. J Opt Commun Netw, 2018, 10: D100
Zhao Y L, Yan B Y, Liu D M, et al. SOON: self-optimizing optical networks with machine learning. Opt Express, 2018, 26: 28713
Xie J F, Yu F R, Huang T, et al. A survey of machine learning techniques applied to software defined networking (SDN): research issues and challenges. IEEE Commun Surv Tut, 2019, 21: 393–430
Musumeci F, Rottondi C, Nag A, et al. An overview on application of machine learning techniques in optical networks. IEEE Commun Surv Tut, 2019, 21: 1383–1408
Khan F N, Fan Q R, Lu C, et al. An optical communication’s perspective on machine learning and its applications. J Lightw Technol, 2019, 37: 493–516
Kaelbling L P, Littman M L, Moore A W. An introduction to reinforcement learning. IEEE Press, 1995, 10: 90–127
Boertjes D W, Reimer M, Cote D. Practical considerations for near-zero margin network design and deployment. J Opt Commun Netw, 2019, 11: 25
Kashi A S, Zhuge Q B, Cartledge J C, et al. Nonlinear signal-to-noise ratio estimation in coherent optical fiber transmission systems using artificial neural networks. J Lightw Technol, 2018, 36: 5424–5431
Li B J, Zhu Z Q. DeepCoop: leveraging cooperative DRL agents to achieve scalable network automation for multidomain SD-EONs. In: Proceedings of Optical Fiber Communication Conference, 2020. TH2A.29
Auge J L. Can we use flexible transponders to reduce margins? In: Proceedings of Optical Fiber Communication Conference, 2013. OTu2A.1
Poggiolini P, Bosco G, Carena A, et al. The GN-model of fiber non-linear propagation and its applications. J Lightw Technol, 2014, 32: 694–721
Pesic J, Lonardi M, Rossi N, et al. How uncertainty on the fiber span lengths influences QoT estimation using machine learning in WDM networks. In: Proceedings of Optical Fiber Communication Conference, 2020. Th3D.5
Sartzetakis I, Christodoulopoulos K K, Varvarigos E M. Accurate quality of transmission estimation with machine learning. J Opt Commun Netw, 2019, 11: 140
Meng F, Yan S, Nikolovgenis K, et al. Field trial of Gaussian process learning of function-agnostic channel performance under uncertainty. In: Proceedings of Optical Fiber Communication Conference, 2018. W4F.5
Yan S Y, Khan F N, Mavromatis A, et al. Field trial of machine-learning-assisted and SDN-based optical network management. In: Proceedings of Optical Fiber Communication Conference, 2019. M2E.1
Mo W Y, Huang Y K, Zhang S L, et al. ANN-based transfer learning for QoT prediction in real-time mixed line-rate systems. In: Proceedings of Optical Fiber Communication Conference, 2018. W4F.3
Rottondi C, Barletta L, Giusti A, et al. Machine-learning method for quality of transmission prediction of unestablished lightpaths. J Opt Commun Netw, 2018, 10: A286
Panayiotou T, Savva G, Shariati B, et al. Machine learning for QoT estimation of unseen optical network states. In: Proceedings of Optical Fiber Communication Conference, 2019. Tu2E.2
Morais R M, Pedro J. Machine learning models for estimating quality of transmission in DWDM networks. J Opt Commun Netw, 2018, 10: D84
Azzimonti D, Rottondi C, Giusti A, et al. Active vs transfer learning approaches for QoT estimation with small training datasets. In: Proceedings of Optical Fiber Communication Conference, 2020. M4E.1
Liu C Y, Chen X L, Proietti R, et al. Evol-tl: evolutionary transfer learning for QoT estimation in multi-domain networks. In: Proceedings of Optical Fiber Communication Conference, 2020. Th3D.1
Zhuge Q B, Zeng X B, Lun H Z, et al. Application of machine learning in fiber nonlinearity modeling and monitoring for elastic optical networks. J Lightw Technol, 2019, 37: 3055–3063
Tremblay C, Allogba S, Aladin S. Quality of transmission estimation and performance prediction of lightpaths using machine learning. In: Proceedings of European Conference on Optical Communication, 2019. 1–3
Seve E, Pesic J, Delezoide C, et al. Learning process for reducing uncertainties on network parameters and design margins. J Opt Commun Netw, 2018, 10: 298
Bouda M, Oda S, Vassilieva O, et al. Accurate prediction of quality of transmission based on a dynamically configurable optical impairment model. J Opt Commun Netw, 2018, 10: A102
Mahajan A, Christodoulopoulos K, Martinez R, et al. Machine learning assisted EDFA gain ripple modelling for accurate QoT estimation. In: Proceedings of European Conference on Optical Communication, 2019. 1–4
D’Amico A, Straullu S, Nespola A, et al. Machine-learning aided OSNR prediction in optical line systems. In: Proceedings of European Conference on Optical Communication, 2019. 1–4
Liu X M, Lun H Z, Fu M F, et al. A three-stage training framework for customizing link models for optical networks. In: Proceedings of Optical Fiber Communication Conference, 2020. Th3D.6
Zefreh M S, Asselin S. Capacity enhancement in optical networks using margin extraction. In: Proceedings of Optical Fiber Communications Conference, 2018. W4A.3
Delezoide C, Christodoulopoulos K, Kretsis A, et al. Marginless operation of optical networks. J Lightw Technol, 2019, 37: 1698–1705
Pesic J, Rossi N, Zami T. Impact of margins evolution along ageing in elastic optical networks. J Lightw Technol, 2019, 37: 4081–4089
Soumplis P, Christodoulopoulos K, Quagliotti M, et al. Multi-period planning with actual physical and traffic conditions. J Opt Commun Netw, 2018, 10: A144
Roberts I, Kahn J M, Boertjes D. Convex channel power optimization in nonlinear WDM systems using gaussian noise model. J Lightw Technol, 2016, 34: 3212–3222
Roberts I, Kahn J M, Harley J, et al. Channel power optimization of WDM systems following Gaussian noise nonlinearity model in presence of stimulated raman scattering. J Lightw Technol, 2017, 35: 5237–5249
Roberts I, Kahn J M. Efficient discrete rate assignment and power optimization in optical communication systems following the gaussian noise model. J Lightw Technol, 2017, 35: 4425–4437
Roberts I, Kahn J M. Measurement-based optimization of channel powers with non-Gaussian nonlinear interference noise. J Lightw Technol, 2018, 36: 2746–2756
Lonardi M, Ramantanis P, Jennevé P, et al. Optical nonlinearity monitoring and launched power optimization by artificial neural networks. In: Proceedings of European Conference on Optical Communication, 2019. 1–4
Huang Y S, Samoud W, Gutterman C L, et al. A machine learning approach for dynamic optical channel add/drop strategies that minimize EDFA power excursions. In: Proceedings of European Conference on Optical Communication, 2016. 1–3
Ahsan A S, Browning C, Wang M S, et al. Excursion-free dynamic wavelength switching in amplified optical networks. J Opt Commun Netw, 2015, 7: 898–905
Lin P J. Reducing optical power variation in amplified optical network. In: Proceedings of International Conference on Communication Technology Proceedings, 2003. 42–47
Zhu S X, Gutterman C L, Mo W Y, et al. Machine learning based prediction of erbium-doped fiber WDM line amplifier gain spectra. In: Proceedings of European Conference on Optical Communication, 2018. 1–3
You Y R, Jiang Z P, Janz C. Machine learning-based EDFA gain model. In: Proceedings of European Conference on Optical Communication, 2018. 1–3
Huang Y S, Gutterman C L, Samadi P, et al. Dynamic mitigation of EDFA power excursions with machine learning. Opt Express, 2017, 25: 2245
Freire M, Mansfeld S, Amar D, et al. Predicting optical power excursions in erbium doped fiber amplifiers using neural networks. In: Proceedings of Asia Communications and Photonics Conference, 2018. 1–3
Gutterman C L, Mo W Y, Zhu S X, et al. Neural network based wavelength assignment in optical switching. In: Proceedings of the Workshop on Big Data Analytics and Machine Learning for Data Communication Networks, 2017. 37–42
Delezoide C, Christodoulopoulos K, Kretsis A, et al. Pre-emptive detection and localization of failures towards marginless operations of optical networks. In: Proceedings of International Conference on Transparent Optical Networks, 2018. 1–4
Ba S, Chatterjee B C, Okamoto S, et al. Route partitioning scheme for elastic optical networks with hitless defragmentation. J Opt Commun Netw, 2016, 8: 356–370
Huang Y S, Cho P B, Samadi P, et al. Dynamic power pre-adjustments with machine learning that mitigate EDFA excursions during defragmentation. In: Proceedings of Optical Fiber Communication Conference, 2017. Th1J.2
Huang Y S, Cho P B, Samadi P, et al. Power excursion mitigation for flexgrid defragmentation with machine learning. J Opt Commun Netw, 2018, 10: 69
Christodoulopoulos K, Tomkos I, Varvarigos E A. Elastic bandwidth allocation in flexible OFDM-based optical networks. J Lightw Technol, 2011, 29: 1354–1366
Wu J J, Ning Z L, Guo L. Energy-efficient survivable grooming in software-defined elastic optical networks. IEEE Access, 2017. doi: https://doi.org/10.1109/ACCESS.2017.2674963
Wang Y, Cao X J, Pan Y. A study of the routing and spectrum allocation in spectrum-sliced elastic optical path networks. In: Proceedings of IEEE INFOCOM, 2011. 1503–1511
Panayiotou T, Manousakis K, Chatzis S P, et al. A data-driven bandwidth allocation framework with QoS considerations for EONs. J Lightw Technol, 2019, 37: 1853–1864
Chen X L, Proietti R, Yoo S J B. Building autonomic elastic optical networks with deep reinforcement learning. IEEE Commun Mag, 2019, 57: 20–26
Troia S, Rodriguez A, Martin I, et al. Machine-learning-assisted routing in SDN-based optical networks. In: Proceedings of European Conference on Optical Communication, 2018. 1–3
Yan B Y, Zhao Y L, Li Y J, et al. Actor-critic-based resource allocation for multi-modal optical networks. In: Proceedings of IEEE Globecom Workshops, 2018. 1–6
Shiraki R, Mori Y, Hasegawa H, et al. Dynamic control of transparent optical networks with adaptive state-value assessment enabled by reinforcement learning. In: Proceedings of International Conference on Transparent Optical Networks, 2019. 1–4
Luo X, Shi C, Chen X, et al. Comprehensive performance study of elastic optical networks for distributed datacenter with survivability. In: Proceedings of Optical Fiber Communication Conference, 2019. Th2A.23
Shiraki R, Mori Y, Hasegawa H, et al. Dynamically controlled flexible-grid networks based on semi-flexible spectrum assignment and network-state-value evaluation. In: Proceedings of Optical Fiber Communication Conference, 2020. M1B.4
Luong N C, Hoang D T, Gong S M, et al. Applications of deep reinforcement learning in communications and networking: a survey. IEEE Commun Surv Tut, 2019, 21: 3133–3174
Luo X, Shi C, Wang L Q, et al. Leveraging double-agent-based deep reinforcement learning to global optimization of elastic optical networks with enhanced survivability. Opt Express, 2019, 27: 7896
Chen X L, Proietti R, Liu C Y, et al. Exploiting multi-task learning to achieve effective transfer deep reinforcement learning in elastic optical networks. In: Proceedings of Optical Fiber Communication Conference, 2020. M1B.3
Chen X L, Li B J, Proietti R, et al. DeepRMSA: a deep reinforcement learning framework for routing, modulation and spectrum assignment in elastic optical networks. J Lightw Technol, 2019, 37: 4155–4163
Suárez-V J, Mestres A, Yu J L, et al. Feature engineering for deep reinforcement learning based routing. In: Proceedings of IEEE International Conference on Communications, 2019. 1–6
Suárez-V J, Mestres A, Yu J, et al. Routing in optical transport networks with deep reinforcement learning. J Opt Commun Netw, 2019, 11: 547
Suárez-V J, Mestres A, Yu J L, et al. Routing based on deep reinforcement learning in optical transport networks. In: Proceedings of Optical Fiber Communication Conference, 2019. M2A.6
Almasan P, Suárez-V J, Badia-S A, et al. Deep reinforcement learning meets graph neural networks: an optical network routing use case. 2019. ArXiv: 1910.07421
Varughese S, Lippiatt D, Richter T, et al. Identification of soft failures in optical links using low complexity anomaly detection. In: Proceedings of Optical Fiber Communication Conference, 2019. W2A.46
Shu L, Yu Z M, Wan Z Q, et al. Low-complexity dual-stage soft failure detection by exploiting digital spectrum information. In: Proceedings of European Conference on Optical Communication, 2019. 1–4
Chen X L, Li B J, Proietti R, et al. Self-taught anomaly detection with hybrid unsupervised/supervised machine learning in optical networks. J Lightw Technol, 2019, 37: 1742–1749
Vela A P, Shariati B, Ruiz M, et al. Soft failure localization during commissioning testing and lightpath operation. J Opt Commun Netw, 2018, 10: A27
Shahkarami S, Musumeci F, Cugini F, et al. Machine-learning-based soft-failure detection and identification in optical networks. In: Proceedings of Optical Fiber Communication Conference, 2018. M3A.5
Lun H Z, Zhuge Q B, Fu M F, et al. Soft failure identification in optical networks based on convolutional neural network. In: Proceedings of European Conference on Optical Communication, 2019. 1–3
Varughese S, Lippiatt D, Richter T, et al. Low complexity soft failure detection and identification in optical links using adaptive filter coefficients. In: Proceedings of Optical Fiber Communication Conference, 2020. M2J.4
Ruiz M, Fresi F, Vela A, et al. Service-triggered failure identification/localization through monitoring of multiple parameters. In: Proceedings of European Conference on Optical Communication, 2016. 1–3
Zhang C Y, Wang D S, Song C, et al. Interpretable learning algorithm based on XGboost for fault prediction in optical network. In: Proceedings of Optical Fiber Communication Conference, 2020. Th1F.3
Panayiotou T, Chatzis S P, Ellinas G. Leveraging statistical machine learning to address failure localization in optical networks. J Opt Commun Netw, 2018, 10: 162
Christodoulopoulos K, Sambo N, Varvarigos E. Exploiting network Kriging for fault localization. In: Proceedings of Optical Fiber Communication Conference, 2016. W1B.5
Li Z T, Zhao Y L, Li Y J, et al. Demonstration of fault localization in optical networks based on knowledge graph and graph neural network. In: Proceedings of Optical Fiber Communication Conference, 2020. Th1F.5
Lun H Z, Liu X M, Cai M, et al. Anomaly localization in optical transmissions based on receiver DSP and artificial neural network. In: Proceedings of Optical Fiber Communication Conference, 2020. W1K.4
Yan S Y, Khan F N, Mavromatis A, et al. Field trial of machine-learning-assisted and SDN-based optical network planning with network-scale monitoring database. In: Proceedings of European Conference on Optical Communication, 2017. 1–3
Christodoulopoulos K, Kokkinos P, Di G A, et al. ORCHESTRA—optical performance monitoring enabling flexible networking. In: Proceedings of International Conference on Transparent Optical Networks, 2015. 1–4
Wang Z L, Zhang M, Wang D S, et al. Failure prediction using machine learning and time series in optical network. Opt Express, 2017, 25: 18553–18565
Boitier F, Lemaire V, Pesic J, et al. Proactive fiber damage detection in real-time coherent receiver. In: Proceedings of European Conference on Optical Communication, 2017. 1–4
Tanaka T, Kawakami W, Kuwabara S, et al. Intelligent monitoring of optical fiber bend using artificial neural networks trained with constellation data. IEEE Netw Lett, 2019, 1: 60–62
Vellido A, Martín-G J D, Lisboa P J G. Making machine learning models interpretable. In: Proceedings of European Symposium on Artificial Neural Networks, 2012. 1–10
Azzimonti D, Rottondi C, Tornatore M. Reducing probes for quality of transmission estimation in optical networks with active learning. J Opt Commun Netw, 2020, 12: 38
Zhao Y L, Yan B Y, Li Z T, et al. Coordination between control layer AI and on-board AI in optical transport networks. J Opt Commun Netw, 2019, 12: 49
Velasco L, Shariati B, Boitier F, et al. Learning life cycle to speed up autonomic optical transmission and networking adoption. J Opt Commun Netw, 2019, 11: 226–237
Acknowledgements
This work was supported by National Natural Science Foundation of China (Grant No. 61801291), Shanghai Rising-Star Program (Grant No. 19QA1404600), and National Key R&D Program of China (Grant No. 2018YFB-1801200).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Gao, R., Liu, L., Liu, X. et al. An overview of ML-based applications for next generation optical networks. Sci. China Inf. Sci. 63, 160302 (2020). https://doi.org/10.1007/s11432-020-2874-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s11432-020-2874-y