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Available Issues

February 19, 2019 - May 1, 2026

Available Issues

February 19, 2019 - May 1, 2026

Dynamic Routing and Wavelength Assignment with Reinforcement Learning

Published Online:8 Jun 2023https://doi.org/10.1287/ijoo.2023.0092

References

  • Almasan P, Suárez-Varela J, Rusek K, Barlet-Ros P, Cabellos-Aparicio A (2022) Deep reinforcement learning meets graph neural networks: Exploring a routing optimization use case. Comput. Comm. 196:184–194.Google Scholar
  • Amin R, Rojas E, Aqdus A, Ramzan S, Casillas-Perez D, Arco JM (2021) A survey on machine learning techniques for routing optimization in SDN. IEEE Access 9:104582–104611.Google Scholar
  • Cappart Q, Chételat D, Khalil E, Lodi A, Morris C, Veličković P (2021) Combinatorial optimization and reasoning with graph neural networks. Preprint, submitted April 27, https://arxiv.org/abs/2102.09544.Google Scholar
  • Chen X, Li B, Proietti R, Lu H, Zhu Z, Yoo SJB (2019) DeepRMSA: A deep reinforcement learning framework for routing, modulation and spectrum assignment in elastic optical networks. J. Lightwave Tech. 37(16):4155–4163.Google Scholar
  • Chu X, Li B, Zhang Z (2003) A dynamic RWA algorithm in a wavelength-routed all-optical network with wavelength converters. Proc. 22nd Annual Joint Conf. IEEE Comput. Comm. Soc., vol. 3 (IEEE, Piscataway, NJ), 1795–1804.Google Scholar
  • Claffy KC, Braun HW, Polyzos GC (1994) Tracking long-term growth of the NSFNET. Comm. ACM 37(8):34–45.Google Scholar
  • Daryalal M, Bodur M (2020) Stochastic RWA and lightpath rerouting in WDM networks. Preprint, submitted December 18, https://arxiv.org/abs/2012.10084.Google Scholar
  • Di Ianni M (1998) Efficient delay routing. Theoretical Comput. Sci. 196(1–2):131–151.Google Scholar
  • Duhovnikov S, Schupke DA, Lehmann G, Fischer T, Rambach F (2006) Dynamic RWA for all optical networks using linear constraints for optical path feasibility assessment. Proc. Eur. Conf. Optical Comm. (IEEE, Piscataway, NJ), 1–2.Google Scholar
  • Freeman LC (1977) A set of measures of centrality based on betweenness. Sociometry 40(1):35–41.Google Scholar
  • Gilmer J, Schoenholz SS, Riley PF, Vinyals O, Dahl GE (2017) Neural message passing for quantum chemistry. Proc. Internat. Conf. on Machine Learn. (PMLR), 1263–1272.Google Scholar
  • Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. Proc. 13th Internat. Conf. Artificial Intelligence Statist. (PMLR), 249–256.Google Scholar
  • Glorot X, Bordes A, Bengio Y (2011) Deep sparse rectifier neural networks. Proc. 14th Internat. Conf. Artificial Intelligence Statist. (PMLR), 315–323.Google Scholar
  • Gu R, Yang Z, Ji Y (2020) Machine learning for intelligent optical networks: A comprehensive survey. J. Network Comput. Appl. 157:102576.Google Scholar
  • Huimin G, Wen Y, Liang W, Zhengtang L (2017) Research on dynamic routing and wavelength assignment algorithm for optical networks. Proc. IEEE 2nd Advanced Inform. Techn., Electronic and Automation Control Conf. (IEEE, Piscataway, NJ), 221–224.Google Scholar
  • Jaumard B, Daryalal M (2017) Efficient spectrum utilization in large scale RWA problems. IEEE/ACM Trans. Networking 25(2):1263–1278.Google Scholar
  • Jaumard B, Meyer C, Yu X (2005) When is wavelength conversion contributing to reducing the blocking rate? Proc. IEEE Global Telecomm. Conf., vol. 4 (IEEE, Piscataway, NJ), 2078–2083.Google Scholar
  • Jaumard B, Meyer C, Yu X (2006) How much wavelength conversion allows a reduction in the blocking rate? J. Optical Networks 5(12):881–900.Google Scholar
  • Kamiński K, Ludwiczak J, Jasiński M, Bukala A, Madaj R, Szczepaniak K, Dunin-Horkawicz S (2022) Rossmann-toolbox: A deep learning-based protocol for the prediction and design of cofactor specificity in rossmann fold proteins. Briefings Bioinformatics 23(1):bbab371.Google Scholar
  • Kingma DP, Ba JL (2015) Adam: A method for stochastic optimization. Proc. 3rd Internat. Conf. Learn. Representations, https://doi.org/10.48550/arXiv.1412.6980.Google Scholar
  • Kiran YV, Venkatesh T, Ram Murthy CS (2007) A reinforcement learning framework for path selection and wavelength selection in optical burst switched networks. IEEE J. Selected Areas Comm. 25(9):18–26.Google Scholar
  • Martin I, Troia S, Hernandez JA, Rodriguez A, Musumeci F, Maier G, Alvizu R, et al. (2019) Machine learning-based routing and wavelength assignment in software-defined optical networks. IEEE Trans. Network Service Management 16(3):871–883.Google Scholar
  • Mnih V, Kavukcuoglu K, Silver D, Graves A, Antonoglou I, Wierstra D, Riedmiller M (2013) Playing Atari with deep reinforcement learning. Preprint, submitted December 19, https://arxiv.org/abs/1312.5602.Google Scholar
  • Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, et al. (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529–533.Google Scholar
  • Mohan N, Kaushal P (2020) Dynamic routing and wavelength assignment for efficient traffic grooming. J. Optical Comm., ePub ahead of print August 24, https://doi.org/10.1515/joc-2019-0309.Google Scholar
  • Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, Killeen T, et al. (2019) Pytorch: An imperative style, high-performance deep learning library. Advances in Neural Information Processing Systems (NeurIPS, San Diego), 8026–8037.Google Scholar
  • Pinto A (2002) Optical networks: A practical perspective, 2nd ed. J. Optical Networking 1(6):219–220.Google Scholar
  • Pointurier Y, Heidari F (2007) Reinforcement learning based routing in all-optical networks with physical impairments. Proc. 4th Internat. Conf. Broadband Comm., Networks Systems (IEEE, Piscataway, NJ), 928–930.Google Scholar
  • Pouya H (2018) New models and algorithms in telecommunication networks. PhD thesis, Concordia University, Montreal.Google Scholar
  • Ramaswami R, Sivarajan KN (1995) Routing and wavelength assignment in all-optical networks. IEEE/ACM Trans. Networking 3(5):489–500.Google Scholar
  • Randhawa R, Sohal JS (2010) Blocking probability analysis of survivable routing in WDM optical networks with/without sparse-wavelength conversion. Optik 121(5):462–466.Google Scholar
  • Rodriguez A, Fernández W, Ramírez L (2016) RWA: Novel Heuristic Algorithm for Optical Networks with Dynamic Traffic BT: Advanced Computer and Communication Engineering Technology (Springer International Publishing, Cham, Switzerland).Google Scholar
  • Rusek K, Suárez-Varela J, Almasan P, Barlet-Ros P, Cabellos-Aparicio A (2020) RouteNet: Leveraging graph neural networks for network modeling and optimization in SDN. IEEE J. Selected Areas Comm. 38(10):2260–2270.Google Scholar
  • Rusek K, Suárez-Varela J, Mestres A, Barlet-Ros P, Cabellos-Aparicio A (2019) Unveiling the potential of graph neural networks for network modeling and optimization in SDN. Proc. ACM Sympos. SDN Res. (ACM, New York), 140–151.Google Scholar
  • Scarselli F, Gori M, Tsoi AC, Hagenbuchner M, Monfardini G (2009) The graph neural network model. IEEE Trans. Neural Networks 20(1):61–80.Google Scholar
  • Schaul T, Quan J, Antonoglou I, Silver D (2016) Prioritized experience replay. Proc. 4th Internat. Conf. on Learn. Representations, http://arxiv.org/abs/1511.05952.Google Scholar
  • Suárez-Varela J, Mestres A, Yu J, Kuang L, Feng H, Cabellos-Aparicio A, Barlet-Ros P (2019) Routing in optical transport networks with deep reinforcement learning. J. Optical Comm. Networks 11(11):547–558.Google Scholar
  • Sutton RS, Barto AG (2018) Reinforcement Learning: An Introduction (MIT Press, Cambridge, MA).Google Scholar
  • Swaminathan A, Chaba M, Sharma DK, Ghosh U (2021) GraphNET: Graph neural networks for routing optimization in software defined networks. Comput. Comm. 178:169–182.Google Scholar
  • Veličković P, Casanova A, Liò P, Cucurull G, Romero A, Bengio Y (2018) Graph attention networks. Proc. 6th Internat. Conf. on Learn. Representations, https://doi.org/10.48550/arXiv.1511.05952.Google Scholar
  • Wang Y, Cao X, Pan Y (2011) A study of the routing and spectrum allocation in spectrum-sliced Elastic Optical Path networks. Proc. IEEE INFOCOM (IEEE, Piscataway, NJ), 1503–1511.Google Scholar
  • Watkins CJCH, Dayan P (1992) Q-learning. Machine Learn. 8(3–4):279–292.Google Scholar
  • Weiss K, Khoshgoftaar TM, Wang D (2016) A survey of transfer learning. J. Big Data 3(1):1–40.Google Scholar
  • Xu Z, Tang J, Meng J, Zhang W, Wang Y, Liu CH, Yang D (2018) Experience-driven networking: A deep reinforcement learning based approach. Proc. IEEE Conf. Comput. Comm. (IEEE, Piscataway, NJ), 1871–1879.Google Scholar
  • Zang H, Jue JP, Mukherjee B (2000) A review of routing and wavelength assignment approaches for wavelength-routed optical WDM networks. Optical Networks Magazine 1(1):47–60.Google Scholar
  • Zang H, Jue JP, Sahasrabuddhe L, Ramamurthy R, Mukherjee B (2001) Dynamic lightpath establishment in wavelength routed WDM networks. IEEE Comm. Magazine 39(9):100–108.Google Scholar
  • Zhou J, Yuan X (2002) A study of dynamic routing and wavelength assignment with imprecise network state information. Proc. Internat. Conf. Parallel Processing Workshop (IEEE, Piscataway, NJ), 207–213.Google Scholar
  • Zhou X, Lu W, Gong L, Zhu Z (2012) Dynamic RMSA in elastic optical networks with an adaptive genetic algorithm. Proc. IEEE Global Comm. Conf. (IEEE, Piscataway, NJ), 2912–2917.Google Scholar
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