Skip to main content
Log in

Towards converged, collaborative and co-automatic (3C) optical networks

  • Review
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

The interconnection of all things is developing a new diagram of future information networks. However, it is difficult to realize future applications with only one single technique. Collaboration between multiple advanced techniques is leading the way for the development of future information networks. Optical communication is an enabling technique to achieve high speed, long reach, and low latency communication, which plays an important role on the transformation of information networks. To achieve these advantages that caters to the characteristics of future information networks, collaboration of multiple advanced techniques with optical, which is called “optical plus X”, could realize the vision of “all things connected with networks”. In this paper, we focus on the collaboration between optical networks with other techniques, mainly discuss four representative aspects, which are “optical plus IP”, “optical plus radio”, “optical plus computing”, and “optical plus AI”. We discuss the challenges, timely works, and developing trends. Finally, we give the future visions for optical network towards a collaborative, converged and co-automatic optical network.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. Yan S Y, Hugues-Salas E, Ou Y N, et al. Hardware-programmable optical networks. Sci China Inf Sci, 2016, 59: 102301

    Google Scholar 

  2. Guo P X, Hou W G, Guo L. Designs of low insertion loss optical router and reliable routing for 3D optical networkon-chip. Sci China Inf Sci, 2016, 59: 102302

    Google Scholar 

  3. Gkamas V, Christodoulopoulos K, Vergados D J, et al. Energy-minimized design of IP over flexible optical networks. Int J Commun Syst, 2017, 30: 3032

    Google Scholar 

  4. Tanaka T, Hirano A, Jinno M. Advantages of IP over elastic optical networks using multi-flow transponders from cost and equipment count aspects. Opt Express, 2014, 22: 62

    Google Scholar 

  5. Tucker R S, Parthiban R, Baliga J, et al. Evolution of WDM optical IP networks: a cost and energy perspective. J Lightwave Technol, 2009, 27: 243–252

    Google Scholar 

  6. Lu W, Yin X F, Cheng X B, et al. On cost-efficient integrated multilayer protection planning in IP-over-EONs. J Lightwave Technol, 2018, 36: 2037–2048

    Google Scholar 

  7. Qiao C M. Labeled optical burst switching for IP-over-WDM integration. IEEE Commun Mag, 2000, 38: 104–114

    Google Scholar 

  8. Sun W Q, Xie G W, Jin Y H, et al. A cross-layer optical circuit provisioning framework for data intensive IP end hosts. IEEE Commun Mag, 2008, 46: 30–37

    Google Scholar 

  9. Kretsis A, Corazza L, Christodoulopoulos K, et al. An emulation environment for SDN enabled flexible IP/optical networks. In: Proceedings of the 18th International Conference on Transparent Optical Networks (ICTON), 2016

    Google Scholar 

  10. Melle S, Ahuja S, Turkcu O, et al. Comparison of converged packet-optical core network architectures. In: Proceedings of Optical Fiber Communications Conference and Exhibition (OFC), 2014

    Google Scholar 

  11. Autenrieth A, Elbers J P, Schmidtke H J, et al. Benefits of integrated packet/circuit/wavelength switches in nextgeneration optical core networks. In: Proceedings of Optical Fiber Communication Conference and Exposition (OFC/NFOEC), 2011

    Google Scholar 

  12. Zhang J W, Ji Y F, Song M, et al. Dynamic traffic grooming in sliceable bandwidth-variable transponder-enabled elastic optical networks. J Lightwave Technol, 2015, 33: 183–191

    Google Scholar 

  13. Zhang J W, Zhao Y L, Yu X S, et al. Energy-efficient traffic grooming in sliceable-transponder-equipped IP-over-elastic optical networks. J Opt Commun Netw, 2015, 7: 142–152

    Google Scholar 

  14. Zhang S Q, Tornatore M, Shen G X, et al. Evolution of traffic grooming from SDH/SONET to flexible grid. In: Proceedings of the 39th European Conference and Exhibition on Optical Communication (ECOC 2013), 2013

    Google Scholar 

  15. Tang F X, Li L F, Chen B W, et al. Mixed channel traffic grooming in shared backup path protected IP over elastic optical network. In: Proceedings of Optical Fiber Communications Conference and Exhibition (OFC), 2017

    Google Scholar 

  16. Yetginer E, Rouskas G N. Power efficient traffic grooming in optical WDM networks. In: Proceedings of Global Telecommunications Conference, 2009

    Google Scholar 

  17. 5G PPP AWG. View on 5G Architecture. v. 1.0, 2016. https://5g-ppp.eu/

  18. China Mobile Research Institute. C-RAN: the Road towards Green RAN. 2011. https://pdfs.semanticscholar.org/eaa3/ca62c9d5653e4f2318aed9ddb8992a505d3c.pdf

  19. CPRI. Common Public Radio Interface (CPRI) Specification. v. 6.1, 2014. http://www.cpri.info

  20. Pizzinat A, Chanclou P, Saliou F, et al. Things you should know about fronthaul. J Lightwave Technol, 2015, 33: 1077–1083

    Google Scholar 

  21. Yang T, Liu W T, Chen X, et al. Modulation format independent blind polarization demultiplexing algorithms for elastic optical networks. Sci China Inf Sci, 2017, 60: 022305

    Google Scholar 

  22. Peng L M, Park K, Youn C H. Investigation on static routing and resource assignment of elastic all-optical switched intra-datacenter networks. Sci China Inf Sci, 2016, 59: 102304

    Google Scholar 

  23. Ji Y F, Zhang J W, Zhao Y L, et al. Prospects and research issues in multi-dimensional all optical networks. Sci China Inf Sci, 2016, 59: 101301

    Google Scholar 

  24. Zou J, Wagner C, Eiselt M. Optical fronthauling for 5G mobile: a perspective of passive metro WDM technology. In: Proceedings of Optical Fiber Communications Conference and Exhibition (OFC), 2017

    Google Scholar 

  25. Diallo T, Pizzinat A, Saliou F, et al. Self-seeded DWDM solution for fronthaul links in centralized-radio access network. J Lightwave Technol, 2016, 34: 4965–4971

    Google Scholar 

  26. Tayq Z, Le Guyader B, Chanclou P, et al. Fronthaul performance demonstration in a WDM-PON-based convergent network. In: Proceedings of European Conference on Networks and Communications (EuCNC), 2016

    Google Scholar 

  27. Yoshima S, Katsumata T, Miura H, et al. Experimental investigation of an optically-superimposed AMCC in 100 Gb/s coherent WDM-PON for 5G mobile fronthaul. In: Proceedings of the 42nd European Conference on Optical Communication, 2016

    Google Scholar 

  28. Kondepu K, Zou J, Beldachi A, et al. Performance evaluation of next-generation elastic backhaul with flexible VCSELbased WDM fronthaul. In: Proceedings of European Conference on Optical Communication (ECOC), 2017

    Google Scholar 

  29. Llorente R, Morant M, Garcia-Rodriguez D, et al. Spatial division multiplexing in the short and medium range: from the datacenter to the fronthaul. In: Proceedings of the 19th International Conference on Transparent Optical Networks (ICTON), 2017

    Google Scholar 

  30. Kobayashi T, Ou H, Hisano D, et al. Bandwidth allocation scheme based on simple statistical traffic analysis for TDM-PON based mobile fronthaul. In: Proceedings of Optical Fiber Communications Conference and Exhibition (OFC), 2016

    Google Scholar 

  31. Takahashi K, Nakamura H, Uzawa H, et al. NG-PON2 demonstration with small delay variation and low latency for 5G mobile fronthaul. In: Proceedings of European Conference on Optical Communication (ECOC), 2017

    Google Scholar 

  32. Tashiro T, Kuwano S, Terada J, et al. A novel DBA scheme for TDM-PON based mobile fronthaul. In: Proceedings of Optical Fiber Communications Conference and Exhibition (OFC), 2014

    Google Scholar 

  33. Hatta S, Tanaka N, Sakamoto T. Implementation of ultra-low latency dynamic bandwidth allocation method for TDM-PON. IEICE Commun Express, 2016, 5: 418–423

    Google Scholar 

  34. Anthapadmanabhan N P, Walid A, Pfeiffer T. Mobile fronthaul over latency-optimized time division multiplexed passive optical networks. In: Proceedings of International Conference on Communication Workshop (ICCW), 2015

    Google Scholar 

  35. Hatta S, Tanaka N, Sakamoto T. Feasibility demonstration of low latency DBA method with high bandwidth-efficiency for TDM-PON. In: Proceedings of Optical Fiber Communication Conference, 2017

    Google Scholar 

  36. Zhou S Y, Liu X, Effenberger F, et al. Mobile-PON: a high-efficiency low-latency mobile fronthaul based on functional split and TDM-PON with a unified scheduler. In: Proceedings of Optical Fiber Communication Conference, 2017

    Google Scholar 

  37. Xu M, Liu X, Chand N, et al. Flex-frame timing-critical passive optical networks for delay sensitive mobile and fixed access services. In: Proceedings of Optical Fiber Communication Conference, 2017

    Google Scholar 

  38. Chitimalla D, Kondepu K, Valcarenghi L, et al. 5G fronthaul-latency and jitter studies of CPRI over ethernet. J Opt Commun Netw, 2017, 9: 172–182

    Google Scholar 

  39. Chang C Y, Schiavi R, Nikaein N, et al. Impact of packetization and functional split on C-RAN fronthaul performance. In: Proceedings of IEEE International Conference on Communications (ICC), 2016

    Google Scholar 

  40. Chang C Y, Nikaein N, Spyropoulos T. Impact of packetization and scheduling on C-RAN fronthaul performance. In: Proceedings of Global Communications Conference, 2017

    Google Scholar 

  41. Zhang J W, Ji Y F, Jia S H, et al. Reconfigurable optical mobile fronthaul networks for coordinated multipoint transmission and reception in 5G. J Opt Commun Netw, 2017, 9: 489–497

    Google Scholar 

  42. Zhang J W, Ji Y F, Xu X Z, et al. Energy efficient baseband unit aggregation in cloud radio and optical access networks. J Opt Commun Netw, 2016, 8: 893–901

    Google Scholar 

  43. Zhang J W, Ji Y F, Yu H, et al. Experimental demonstration of fronthaul flexibility for enhanced CoMP service in 5G radio and optical access networks. Opt Express, 2017, 25: 21247–21258

    Google Scholar 

  44. Yu H, Zhang J W, Song D X, et al. Demonstration of lightpath reconfiguration for BBU aggregation in the SDNenabled optical fronthaul networks. In: Proceedings of European Conference on Optical Communication (ECOC), 2017

    Google Scholar 

  45. Wang X B, Cavdar C, Wang L, et al. Joint allocation of radio and optical resources in virtualized cloud RAN with CoMP. In: Proceedings of Global Communications Conference (GLOBECOM), 2016

    Google Scholar 

  46. Wang X B,Wang L, Cavdar C, et al. Handover reduction in virtualized cloud radio access networks using TWDM-PON fronthaul. J Opt Commun Netw, 2016, 8: 124–134

    Google Scholar 

  47. Carapellese N, Tornatore M, Pattavina A. Energy-efficient baseband unit placement in a fixed/mobile converged WDM aggregation network. IEEE J Sel Areas Commun, 2014, 32: 1542–1551

    Google Scholar 

  48. Li Y C, Gao L, Bose S K, et al. Lightpath blocking analysis for optical networks with ROADM intra-node add-drop contention. Sci China Inf Sci, 2016, 59: 102305

    Google Scholar 

  49. Musumeci F, Bellanzon C, Carapellese N, et al. Optimal BBU placement for 5G C-RAN deployment over WDM aggregation networks. J Lightwave Technol, 2016, 34: 1963–1970

    Google Scholar 

  50. Jain R, Paul S. Network virtualization and software defined networking for cloud computing: a survey. IEEE Commun Mag, 2013, 51: 24–31

    Google Scholar 

  51. Hu Z M, Li B C, Luo J. Flutter: scheduling tasks closer to data across geo-distributed datacenters. In: Proceedings of the 35th Annual IEEE International Conference on Computer Communications, 2016

    Google Scholar 

  52. Hung C C, Golubchik L, Yu M. Scheduling jobs across geo-distributed datacenters. In: Proceedings of the 6th ACM Symposium on Cloud Computing, 2015

    Google Scholar 

  53. Chen L, Liu S H, Li B C, et al. Scheduling jobs across geo-distributed datacenters with max-min fairness. In: Proceedings of IEEE International Conference on Computer Communications (INFOCOM), 2017

    Google Scholar 

  54. Yao J J, Lu P, Gong L, et al. On fast and coordinated data backup in geo-distributed optical inter-datacenter networks. J Lightwave Technol, 2015, 33: 3005–3015

    Google Scholar 

  55. Yao J J, Lu P, Zhu Z Q. Minimizing disaster backup window for geo-distributed multi-datacenter cloud systems. In: Proceedings of IEEE International Conference on Communications, 2014

    Google Scholar 

  56. Li W X, Qi H, Li K Q, et al. Joint optimization of bandwidth for provider and delay for user in software defined data centers. IEEE Trans Cloud Comput, 2017, 5: 331–343

    Google Scholar 

  57. Wang Y W, Su S, Jiang S J, et al. Optimal routing and bandwidth allocation for multiple inter-datacenter bulk data transfers. In: Proceedings of IEEE International Conference on Communications, 2012

    Google Scholar 

  58. Liu Y N, Niu D, Li B C. Delay-optimized video traffic routing in software-defined interdatacenter networks. IEEE Trans Multimedia, 2016, 18: 865–878

    Google Scholar 

  59. Gharbaoui M, Martini B, Castoldi P. Anycast-based optimizations for inter-data-center interconnections. J Opt Commun Netw, 2012, 4: 168–178

    Google Scholar 

  60. Takita Y, Hashiguchi T, Tajima K, et al. Towards seamless service migration in network re-optimization for optically interconnected datacenters. Opt Switch Netw, 2017, 23: 241–249

    Google Scholar 

  61. Liu Z, Zhang J W, Bai L, et al. Joint jobs scheduling and routing for metro-scaled micro datacenters over elastic optical networks. In: Proceedings of Optical Fiber Communication Conference, 2018

    Google Scholar 

  62. Fang W J, Zeng M L, Liu X H, et al. Joint spectrum and IT resource allocation for efficient VNF service chaining in inter-datacenter elastic optical networks. IEEE Commun Lett, 2016, 20: 1539–1542

    Google Scholar 

  63. Tran T X, Hajisami A, Pandey P, et al. Collaborative mobile edge computing in 5G networks: new paradigms, scenarios, and challenges. IEEE Commun Mag, 2017, 55: 54–61

    Google Scholar 

  64. Maor I, Gerstel O, Lopez V, et al. First demonstration of SDN-controlled multi-layer restoration and its advantage over optical restoration. In: Proceedings of European Conference on Optical Communication (ECOC), 2016

    Google Scholar 

  65. Liu S Q, Lu W, Zhu Z Q, et al. Cost-efficient multi-layer restoration to address IP router outages in IP-over-EONs. In: Proceedings of Optical Fiber Communications Conference and Exhibition (OFC), 2017

    Google Scholar 

  66. Liu S Q, Li B J, Zhu Z Q. Realizing AI-assisted multi-layer restoration in a software-defined IP-over-EON with deep learning: an experimental study. In: Proceedings of Optical Fiber Communication Conference, 2018

    Google Scholar 

  67. Rafique D, Szyrkowiec T, Griesser H, et al. TSDN-enabled network assurance: a cognitive fault detection architecture. In: Proceedings of European Conference on Optical Communication (ECOC), 2017

    Google Scholar 

  68. Ekanayake N, Herath H M V R. Effect of nonlinear phase noise on the performance of M-ary PSK signals in optical fiber links. J Lightwave Technol, 2013, 31: 447–454

    Google Scholar 

  69. Lau A P T, Kahn J M. Signal design and detection in presence of nonlinear phase noise. J Lightwave Technol, 2007, 25: 3008–3016

    Google Scholar 

  70. Napoli A, Maalej Z, Sleiffer V A J M, et al. Reduced complexity digital back-propagation methods for optical communication systems. J Lightwave Technol, 2014, 32: 1351–1362

    Google Scholar 

  71. Wang D S, Zhang M, Li Z, et al. Nonlinear decision boundary created by a machine learning-based classifier to mitigate nonlinear phase noise. In: Proceedings of European Conference on Optical Communication (ECOC), 2015

    Google Scholar 

  72. Lin P J. Reducing optical power variation in amplified optical network. In: Proceedings of International Conference on Communication Technology Proceedings, 2003

    Google Scholar 

  73. 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

    Google Scholar 

  74. Zhang M Y, Yin Y W, Proietti R, et al. Spectrum defragmentation algorithms for elastic optical networks using hitless spectrum retuning techniques. In: Proceedings of Optical Fiber Communication Conference, 2013

    Google Scholar 

  75. Takagi T, Hasegawa H, Sato K, et al. Disruption minimized spectrum defragmentation in elastic optical path networks that adopt distance adaptive modulation. In: Proceedings of European Conference and Exposition on Optical Communications, 2011

    Google Scholar 

  76. Cugini F, Paolucci F, Meloni G, et al. Push-pull defragmentation without traffic disruption in flexible grid optical networks. J Lightwave Technol, 2013, 31: 125–133

    Google Scholar 

  77. Mo W, 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

    Google Scholar 

  78. Samadi P, Amar D, Lepers C, et al. Quality of transmission prediction with machine learning for dynamic operation of optical WDM networks. In: Proceedings of European Conference on Optical Communication (ECOC), 2017

    Google Scholar 

  79. Dikbiyik F, Tornatore M, Mukherjee B. Minimizing the risk from disaster failures in optical backbone networks. J Lightwave Technol, 2014, 32: 3175–3183

    Google Scholar 

  80. Hou W G, Ning Z L, Guo L, et al. Novel framework of risk-aware virtual network embedding in optical data center networks. IEEE Syst J, 2018, 12: 2473–2482

    Google Scholar 

  81. Hou W G, Ning Z L, Guo L, et al. Service degradability supported by forecasting system in optical data center networks. IEEE Syst J, 2018. doi: 10.1109/JSYST.2018.2821714

    Google Scholar 

  82. Huang S G, Guo B L, Li X, et al. Pre-configured polyhedron based protection against multi-link failures in optical mesh networks. Opt Express, 2014, 22: 2386–2402

    Google Scholar 

  83. Li X, Huang S G, Yin S, et al. Shared end-to-content backup path protection in k-node (edge) content connected elastic optical datacenter networks. Opt Express, 2016, 24: 9446–9464

    Google Scholar 

  84. 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

    Google Scholar 

  85. Christodoulopoulos K, Kokkinos P, Di Giglio A, et al. ORCHESTRA-Optical performance monitoring enabling flexible networking. In: Proceedings of the 17th International Conference on Transparent Optical Networks (ICTON), 2015

    Google Scholar 

  86. Barletta L, Giusti A, Rottondi C, et al. QoT estimation for unestablished lighpaths using machine learning. In: Proceedings of Optical Fiber Communications Conference and Exhibition (OFC), 2017

    Google Scholar 

  87. Chen X L, Guo J N, Zhu Z Q, et al. Deep-RMSA: a deep-reinforcement-learning routing, modulation and spectrum assignment agent for elastic optical networks. In: Proceedings of Optical Fiber Communication Conference, 2018

    Google Scholar 

  88. Chen X L, Guo J N, Zhu Z Q, et al. Leveraging deep learning to achieve knowledge-based autonomous service provisioning in broker-based multi-domain SD-EONs with proactive and intelligent predictions of multi-domain traffic. Traffic, 2017, 50: 150

    Google Scholar 

  89. Ohba T, Arakawa S, Murata M. A Bayesian-based approach for virtual network reconfiguration in elastic optical path networks. In: Proceedings of Optical Fiber Communication Conference, 2017

    Google Scholar 

  90. Box G E P, Tiao G C. Bayesian Inference in Statistical Analysis. Hoboken: John Wiley and Sons, 2011

    MATH  Google Scholar 

  91. Ohba T, Arakawa S, Murata M. Virtual network reconfiguration in elastic optical path networks for future bandwidth allocation. J Opt Commun Netw, 2016, 8: 633–644

    Google Scholar 

Download references

Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant Nos. 61771073, 61501055), National Science and Technology Major Project (Grant No. 2017ZX03001016), Fund of State Key Laboratory of Information Photonics and Optical Communications (Beijing University of Posts and Telecommunications) of China (Grant No. IPOC2017ZT09), and Fundamental Research Funds for the Central Universities.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuefeng Ji.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ji, Y., Zhang, J., Wang, X. et al. Towards converged, collaborative and co-automatic (3C) optical networks. Sci. China Inf. Sci. 61, 121301 (2018). https://doi.org/10.1007/s11432-018-9551-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11432-018-9551-8

Keywords

Navigation