Advertisement

Fragmentation Metrics in Spectrally-Spatially Flexible Optical Networks

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11616)

Abstract

Spectrally-spatially flexible optical networks (SS-FONs) are proposed as a solution for future traffic requirements in optical backbone networks. As SS-FONs operate within flex-grid, the provisioning of lightpaths spanning multiple frequency slots results in spectrum fragmentation, especially in presence of dynamic traffic. Fragmentation, in turn, may lead to blocking of dynamic requests due to the lack of sufficiently-large free spectral windows. In this paper, to reach a better understanding of fragmentation in SS-FON, we extend several metrics used in (single-core) elastic optical networks to measure the fragmentation in SS-FONs. Next, we apply these metrics to a dynamic-routing algorithm with the goal of minimizing bandwidth blocking. Finally, we analyze the impact of spatial continuity constraint (SCC) on the network fragmentation. Simulations run on two representative network topologies show that the root mean square factor metric yields the best performance in terms of blocking when compared to other analyzed metrics.

Keywords

Spectrally-spatially flexible optical networks Network fragmentation Network optimization Routing Space Spectrum allocation 

Notes

Acknowledgments

The work of P. Lechowicz was supported National Science Centre, Poland under Grant 2015/19/B/ST7/02490. The work of A. Włodarczyk and K. Walkowiak was supported by National Science Centre, Poland under Grant 2017/27/B/ST7/00888.

References

  1. 1.
    Amar, D., Le Rouzic, E., Brochier, N., Auge, J.L., Lepers, C., Perrot, N.: Spectrum fragmentation issue in flexible optical networks: analysis and good practices. Photonic Netw. Commun. 29(3), 230–243 (2015)CrossRefGoogle Scholar
  2. 2.
    Chatterjee, B.C., Ba, S., Oki, E.: Fragmentation problems and management approaches in elastic optical networks: a survey. IEEE Commun. Surv. Tut. 20(1), 183–210 (2018)CrossRefGoogle Scholar
  3. 3.
    Fujii, S., Hirota, Y., Tode, H., Murakami, K.: On-demand spectrum and core allocation for reducing crosstalk in multicore fibers in elastic optical networks. IEEE/OSA J. Opt. Commun. Netw. 6(12), 1059–1071 (2014)CrossRefGoogle Scholar
  4. 4.
    Horota, A., et al.: Routing and spectrum assignment algorithm with most fragmented path first in elastic optical networks. In: 7th IEEE Latin-American Conference on Communications (LATINCOM), pp. 1–6 (2015)Google Scholar
  5. 5.
    Khodashenas, P.S., et al.: Comparison of spectral and spatial super-channel allocation schemes for SDM networks. J. Light. Technol. 34(11), 2710–2716 (2016)CrossRefGoogle Scholar
  6. 6.
    Klinkowski, M., Lechowicz, P., Walkowiak, K.: Survey of resource allocation schemes and algorithms in spectrally-spatially flexible optical networking. Opt. Switch. Netw. 27, 58–78 (2018)CrossRefGoogle Scholar
  7. 7.
    Liu, L., et al.: 3D elastic optical networks in temporal, spectral, and spatial domains with fragmentation-aware RSSMA algorithms. In: The European Conference on Optical Communication (ECOC), pp. 1–3 (2014)Google Scholar
  8. 8.
    Marom, D.M., Blau, M.: Switching solutions for WDM-SDM optical networks. IEEE Comm. Mag. 53(2), 60–68 (2015)CrossRefGoogle Scholar
  9. 9.
    Rottondi, C., Tornatore, M., Gavioli, G.: Optical ring metro networks with flexible grid and distance-adaptive optical coherent transceivers. Bell Labs Technol. J. 18(3), 95–110 (2013)CrossRefGoogle Scholar
  10. 10.
    Rumipamba-Zambrano, R., et al.: Space continuity constraint in dynamic flex-grid/SDM optical core networks: an evaluation with spatial and spectral super-channels. Comput. Commun. 126, 38–49 (2018)CrossRefGoogle Scholar
  11. 11.
    Saridis, G.M., Alexandropoulos, D., Zervas, G., Simeonidou, D.: Survey and evaluation of space division multiplexing: from technologies to optical networks. IEEE Commun. Surv. Tut. 17(4), 2136–2156 (2015)CrossRefGoogle Scholar
  12. 12.
    Shariati, B., et al.: Realizing spectrally-spatially flexible optical networks. IEEE Photon. Soc. Newsl. 31(6), 4–9 (2017)Google Scholar
  13. 13.
    Shen, J., Chen, J., Sun, Y.: Fragmentation aware routing and spectrum assignment algorithm for elastic optical networks. In: TENCON 2015–2015 IEEE Region 10 Conference, pp. 1–4 (2015)Google Scholar
  14. 14.
    Sugihara, S., et al.: Dynamic resource allocation for immediate and advance reservation in space-division-multiplexing-based elastic optical networks. IEEE/OSA J. Opt. Commun. Netw. 9(3), 183–197 (2017)CrossRefGoogle Scholar
  15. 15.
    Tode, H., Hirota, Y.: Routing, spectrum, and core and/or mode assignment on space-division multiplexing optical networks [invited]. IEEE/OSA J. Opt. Commun. Netw. 9(1), A99–A113 (2017)CrossRefGoogle Scholar
  16. 16.
    Walkowiak, K., et al.: Dynamic routing in spectrally spatially flexible optical networks with back-to-back regeneration. IEEE/OSA J. Opt. Commun. Netw. 10(5), 523–534 (2018)MathSciNetCrossRefGoogle Scholar
  17. 17.
    Winzer, P.J.: Optical networking beyond WDM. IEEE Photonics J. 4(2), 647–651 (2012)CrossRefGoogle Scholar
  18. 18.
    Winzer, P.J.: Spatial multiplexing in fiber optics: the 10x scaling of metro/core capacities. Bell Labs Technol. J. 19, 22–30 (2014)CrossRefGoogle Scholar
  19. 19.
    Ye, Z., Patel, A.N., Ji, P.N., Qiao, C.: Root mean square (RMS) factor for assessing spectral fragmentation in flexible grid optical networks. In: 2014 OptoElectronics and Communication Conference and Australian Conference on Optical Fibre Technology, pp. 357–358 (2014)Google Scholar
  20. 20.
    Zhao, Y., et al.: Crosstalk-aware spectrum defragmentation based on spectrum compactness in space division multiplexing enabled elastic optical networks with multicore fiber. IEEE Access 6, 15346–15355 (2018).  https://doi.org/10.1109/ACCESS.2018.2795102CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2020

Authors and Affiliations

  1. 1.Department of Systems and Computer NetworksWrocław University of Science and TechnologyWrocławPoland
  2. 2.Department of Electronics, Information, and BioengineeringPolitecnico di MilanoMilanItaly

Personalised recommendations