Skip to main content

Efficient Clustering of Cabinets at FttCab

  • Conference paper
Internet of Things, Smart Spaces, and Next Generation Networking (ruSMART 2013, NEW2AN 2013)

Abstract

At this moment consumers want an internet connection with 20-50 Mb/s speed and around 100 Mb/s in the near future. Rolling out Fibre to the Curb networks quickly will be the only way for telecom operators in some countries to compete with cable tv operators. This requires a fibre connection to the cabinets. When the telecom operator wants to connect the cabinets in a ring structure, he has to decide how to divide cabinets over a number of circuits, taking into account a maximum number of customers per circuit. This we call the cabinet clustering problem. In this paper we formulate this problem, present the heuristic approch we developed and show the results of our extensive testing that shows the method is accurate and fast. Finally we demonstrate the method on a real life case.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Chardy, M., Costa, M.C., Faye, A., Trampont, M.: Optimizing splitter and ber location in a multilevel optical ftth network. European Journal of Operational Research (222), 430–440 (2012)

    Google Scholar 

  2. Kalsch, M.T., Koerkel, M.F., Nitsch, R.: Embedding ring structures in large ber networks. In: XVth International Telecommunications Network Strategy and Planning Symposium (NETWORKS) (2012)

    Google Scholar 

  3. Gollowitzer, S., Gouveia, L., Ljubić, I.: A node splitting technique for two level network design problems with transition nodes. In: Pahl, J., Reiners, T., Voß, S. (eds.) INOC 2011. LNCS, vol. 6701, pp. 57–70. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  4. Gollowitzer, S., Ljubić, I.: Mip models for connected facility location: A theoretical and computational study. Computers and Operations Research 38, 435–449 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  5. Mateus, G.R., Cruz, F.R.B., Luna, H.P.L.: An algorithm for hierarchical network design. Location Science 2(3), 149–164 (1994)

    MATH  Google Scholar 

  6. Mitcsenkov, A., Paksy, G., Cinkler, T.: Topology design and capex estimation for passive optical networks. In: Proceedings of BROADNETS 2009 (2009)

    Google Scholar 

  7. Kou, L., Markowsky, G., Berman, L.: A fast algorithm for steiner trees. Acta Informatica 14, 141–145 (1981)

    Article  MathSciNet  Google Scholar 

  8. Zhao, R., Liu, H., Lehnert, R.: Topology design of hierarchical hybrid ber-vdsl access networks with aco. In: Proceedings of Fourth Advanced International Conference on Telecommunications (2008)

    Google Scholar 

  9. Gódor, I., Magyar, G.: Cost-optimal topology planning of hierarchical access networks. Computers & Operations Research 32(1), 59–86 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  10. MacQueen, J.: Some methods for classi cation and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)

    Google Scholar 

  11. Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recognition Letters 31, 651–666 (2010)

    Article  Google Scholar 

  12. Aloise, D., Deshpande, A., Hansen, P., Popat, P.: Np-hardness of euclidean sum- of-squares clustering. Machine Learning 75, 245–249 (2009)

    Article  Google Scholar 

  13. Inaba, M., Katoh, N., Imai, H.: Applications of weighted voronoi diagrams and randomization to variance-based k-clustering. In: Proceedings of 10th ACM Symposium on Computational Geometry, pp. 332–339 (1994)

    Google Scholar 

  14. Lloyd, S.P.: Least squares quantization in pcm. IEEE Transactions on information theory IT 28(2) (1982)

    Google Scholar 

  15. Bradley, P., Bennet, K., Demiriz, A.: Constrained k-means clustering. Technical report, Microsoft Research MSR-TR-2000-65 (2000)

    Google Scholar 

  16. Arthur, D., Vassilvitskii, S.: k-means++: The advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, pp. 1027–1035 (2007)

    Google Scholar 

  17. Croes, G.: A method for solving traveling salesman problems. Operations Research 6, 791–812 (1958)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Phillipson, F. (2013). Efficient Clustering of Cabinets at FttCab. In: Balandin, S., Andreev, S., Koucheryavy, Y. (eds) Internet of Things, Smart Spaces, and Next Generation Networking. ruSMART NEW2AN 2013 2013. Lecture Notes in Computer Science, vol 8121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40316-3_18

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-40316-3_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-40315-6

  • Online ISBN: 978-3-642-40316-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics