Efficient dynamic routing in Spectrally-Spatially Flexible Optical Networks based on traffic categorization and supervised learning methods

Cisco Company, 2020 Marom, 2017, Survey of photonic switching architectures and technologies in support of spatially and spectrally flexible optical networking [invited], IEEE/OSA Journal of Optical Communications and Networking, 9, 1, 10.1364/JOCN.9.000001 Klinkowski, 2018, Survey of resource allocation schemes and algorithms in spectrally-spatially flexible optical networking, Opt. Switch. Netw., 27, 58, 10.1016/j.osn.2017.08.003 Klonidis, 2015, Spectrally and spatially flexible optical network planning and operations, IEEE Commun. Mag., 53, 69, 10.1109/MCOM.2015.7045393 Marom, 2015, Switching solutions for WDM-SDM optical networks, IEEE Commun. Mag., 2, 60, 10.1109/MCOM.2015.7045392 Goścień, 2015, Protection in elastic optical networks, IEEE Network, 29, 88, 10.1109/MNET.2015.7340430 Goścień, 2019, On the efficient dynamic routing in spectrally-spatially flexible optical networks Zhao, 2016, Crosstalk-aware cross-core virtual concatenation in spatial division multiplexing elastic optical networks, Electron. Lett., 52, 1701, 10.1049/el.2016.2132 Fujii, 2014, On-demand spectrum and core allocation for reducing crosstalk in multicore fibers in elastic optical networks, IEEE/OSA Journal of Optical Communications and Networking, 6, 1059, 10.1364/JOCN.6.001059 Klinkowski, 2019, Dynamic crosstalk-aware lightpath provisioning in spectrally-spatially flexible optical networks, IEEE/OSA Journal of Optical Communications and Networking, 11, 213, 10.1364/JOCN.11.000213 Walkowiak, 2019, Effective worst-case crosstalk estimation for dynamic translucent SDM elastic optical networks Fujii, 2014, Dynamic spectrum and core allocation with spectrum region reducing costs of building modules in AoD nodes Tode, 2017, Routing, spectrum, and core and/or mode assignment on space-division multiplexing optical networks [invited], IEEE/OSA Journal of Optical Communications and Networking, 9, A99, 10.1364/JOCN.9.000A99 Zhao, 2017, An auxiliary graph based dynamic traffic grooming algorithm in spatial division multiplexing enabled elastic optical networks with multi-core fibers, Opt. Fiber Technol., 34, 52, 10.1016/j.yofte.2017.01.005 Rumipamba-Zambrano, 2018, Dynamic traffic grooming in joint switching (JoS)-enabled flex-grid/SDM optical core networks Walkowiak, 2018, Dynamic routing in spectrally spatially flexible optical networks with back-to-back regeneration, IEEE/OSA Journal of Optical Communications and Networking, 10, 523, 10.1364/JOCN.10.000523 Walkowiak, 2018, Survivable routing in spectrally-spatially flexible optical networks with back-to-back regeneration Oliveira, 2018, routing, modulation, core, and spectrum allocation in SDM elastic optical networks, IEEE Commun. Lett., 22, 1806, 10.1109/LCOMM.2018.2850346 Oliveira, 2018, Spectrum overlap and traffic grooming in p-cycle algorithm protected SDM optical networks Oliveira, 2019, Multipath routing, spectrum and core allocation in protected SDM elastic optical networks Musumeci, 2019, An overview on application of machine learning techniques in optical networks, IEEE Communications Surveys Tutorials, 21, 1383, 10.1109/COMST.2018.2880039 Xie, 2019, A survey of machine learning techniques applied to software defined networking (sdn): research issues and challenges, IEEE Communications Surveys Tutorials, 21, 393, 10.1109/COMST.2018.2866942 Balanici, 2019, Machine learning-based traffic prediction for optical switching resource allocation in hybrid intra-data center networks Singh, 2018, Machine-learning-based prediction for resource (re)allocation in optical data center networks, IEEE/OSA Journal of Optical Communications and Networking, 10, D12, 10.1364/JOCN.10.000D12 Salani, 2019, Routing and spectrum assignment integrating machine-learning-based qot estimation in elastic optical networks Ksieniewicz, 2020, Pattern recognition model to aid the optimization of dynamic spectrally-spatially flexible optical networks Khan, 2012, Modulation format identification in heterogeneous fiber-optic networks using artificial neural networks, Opt Express, 20, 12422, 10.1364/OE.20.012422 Natalino, 2019, Experimental study of machine-learning-based detection and identification of physical-layer attacks in optical networks, J. Lightwave Technol., 37, 4173, 10.1109/JLT.2019.2923558 Panayiotou, 2018, Leveraging statistical machine learning to address failure localization in optical networks, IEEE/OSA Journal of Optical Communications and Networking, 10, 162, 10.1364/JOCN.10.000162 Wang, 2018, Scheduling with machine-learning-based flow detection for packet-switched optical data center networks, IEEE/OSA Journal of Optical Communications and Networking, 10, 365, 10.1364/JOCN.10.000365 Rastegarfar, 2016, Tcp flow classification and bandwidth aggregation in optically interconnected data center networks, IEEE/OSA Journal of Optical Communications and Networking, 8, 777, 10.1364/JOCN.8.000777 Viljoen, 2016, Machine learning based adaptive flow classification for optically interconnected data centers Somani, 2011, Dynamic advance reservation with delayed allocation over wavelength-routed networks Goścień, 2018, Artificial bee colony for optimization of cloud-ready and survivable elastic optical networks, Comput. Commun., 128, 35, 10.1016/j.comcom.2018.07.011 Ibrahimi, 2021, Machine learning regression for QoT estimation of unestablished lightpaths, IEEE/OSA Journal of Optical Communications and Networking, 13, B92, 10.1364/JOCN.410694 Khodashenas, 2016, Comparison of spectral and spatial super-channel allocation schemes for SDM networks, J. Lightwave Technol., 34, 2710, 10.1109/JLT.2016.2551299 Walkowiak, 2016 Orlowski, 2010, SNDlib 1.0–survivable network design library, Networks, 55, 276, 10.1002/net.20371 Proietti, 2015, 3D elastic optical networking in the temporal, spectral, and spatial domains, IEEE Commun. Mag., 53, 79, 10.1109/MCOM.2015.7045394 Tode, 2014, Routing, spectrum and core assignment for space division multiplexing elastic optical networks Alpaydin, 2020 Stapor, 2021, How to design the fair experimental classifier evaluation, Appl. Soft Comput., 104, 107219, 10.1016/j.asoc.2021.107219 Mitchell, 1997