Skip to main content
SpringerLink
Log in

Quantifying the Shift in Network Usage Upon Bandwidth Upgrade

  • Conference paper
  • First Online:
e-Infrastructure and e-Services for Developing Countries (AFRICOMM 2021)

Abstract

Traffic flow classification is an important enabler in network design, capacity planning, identification of user requirements and possible tracking of user population growth based on network usage. In this paper, results from the Internet traffic flow characterization in 1 Mbps community network for a three-week snapshot representing three months of study show that during peak traffic, the network is overwhelmed and service degradation occurs. When the network is upgraded to 10 Mbps the network bandwidth utilization immediately increases dramatically to close in on the new capacity with 20% left unused during peak traffic. The situation gets worse one month later where the network utilization is only 3% away from the maximum capacity. Traffic categorization show that the applications crossing the network are legitimate and acceptable. Since 10 Mbps bandwidth is the capacity that is sustainable for the community and supported by existing technology, bandwidth management is essential to ensure the network remains usable and continues to provide acceptable user experience.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Moore, A., Papagiannaki, K.: Toward the accurate identification of network applications. In: Proceedings of Passive and Active Measurement, pp. 41–54. Boston (2005)

    Google Scholar 

  2. Crotti, M., Dusi, M., Gringoli, F., Salgarelli, L.: Traffic classification through simple statistical fingerprinting. ACM SIGCOMM Comput. Commun. Rev. 37(1), 5–16 (2007)

    Article  Google Scholar 

  3. Haffner, P., Sen, S., Spatscheck, O., Wang, D.: ACAS: automated construction of application signatures. In: Proceedings of ACM SIGCOMM Workshop on Mining Network Data, pp. 197–202. Philadelphia (2005)

    Google Scholar 

  4. Bakhshi, T., Ghita, B.: On internet traffic classification: a two-phased machine learning approach. J. Comput. Netw. Commun. 2016, 2048302 (2016)

    Google Scholar 

  5. Karagiannis, T., Papagiannaki, K., Faloutsos, M.: BLINC: multilevel traffic classification in the dark. In: Proceedings of ACM SIGCOMM, pp. 229–240. Philadelphia (2005)

    Google Scholar 

  6. Callado, A., et al.: A survey on internet traffic identification. IEEE Commun. Surv. Tutor. 11(3), 37–52 (2009)

    Article  Google Scholar 

  7. Roughan, M., Sen, S., Spatscheck, O., Duffield, N.: Class-of-service mapping for QoS: a statistical signature-based approach to IP traffic classification. In: ACM SIGCOMM Internet Measurement Conference 2004, Taormina (2004)

    Google Scholar 

  8. Kushida, T.: An empirical study of the characteristics of Internet traffic. Comput. Commun. 22(17), 1607–1618 (1999)

    Article  Google Scholar 

  9. Du, B., Demmer, M., Brewer, E.: Analysis of WWW traffic in Cambodia and Ghana. In: Proceedings of the 15th International Conference on World Wide Web, pp. 771–780. Edinburgh (2006)

    Google Scholar 

  10. Zheleva, M., Schmitt, P., Vigil, M., Belding, E.: The increased bandwidth fallacy: performance and usage in rural Zambia. In: Proceedings of the 4th Annual Symposium on Computing for Development, pp. 1–10. Cape Town (2013)

    Google Scholar 

  11. Vos, E.: Picocela deploys large mesh WiFi in Fukuoka, Japan. Muniwireless Homepage. http://muniwireless.com/2010/03/09/picocela. Accessed 5 Dec 2019

  12. Gupta, A., Kumar, K.: A survey of 5G network: architecture and emerging technologies. IEEE Access 3, 1206–1232 (2015)

    Article  Google Scholar 

  13. Finsterbusch, M., Richter, C., Rocha, E., Muller, J.A., Hanssgen, K.: A survey of payload-based traffic classification approaches. IEEE Commun. Surv. Tutor. 16(2), 1135–1156 (2014)

    Article  Google Scholar 

  14. Antonello, R., et al.: Deep packet inspection tools and techniques in commodity platforms: challenges and trends. J. Netw. Comput. Appl. 35(6), 1863–1878 (2012)

    Article  Google Scholar 

  15. nDPI: Open and Extensible LGPLv3 Deep Packet Inspection Library: NTOP Homepage. https://www.ntop.org/products/deep-packet-inspection/ndpi. Accessed 5 Dec 2019

  16. Johnson, D.L., Belding, E.M., Almeroth, K., van Stam, G.: Internet usage and performance analysis of a rural wireless network in Macha, Zambia. In: Proceedings of the 4th ACM Workshop on Network Systems for Developing Regions, pp. 1–6. San Francisco, California (2010)

    Google Scholar 

  17. Chebrolu, K., Raman, B., Sen, S.: Long-distance 802.11b links: performance measurements and experience. In: Proceedings of the 12th International Conference on Mobile Computing and Networking, pp. 74–85, Los Angeles, California (2006)

    Google Scholar 

  18. Sheth, A., Nedevschi, S., Patra, R., Surana, S., Brewer, E., Subramanian, L.: Packet loss characterization in WiFi-based long distance networks. In: 26th IEEE International Conference on Computer Communication, pp. 312–320. Anchorage, Alaska (2007)

    Google Scholar 

  19. Surana, S., et al.: Beyond pilots: keeping rural wireless networks alive. In: Proceedings of the 5th USENIX Symposium on Networked Systems Design and Implementation, pp. 119–132. San Francisco, California (2008)

    Google Scholar 

  20. Neumann, A., Vilata, I., León, X., Garcia, P.E., Navarro, L., López, E.: Community-lab: architecture of a community networking testbed for the future internet. In: Proceedings of the IEEE 8th International Conference on Wireless and Mobile Computing, Networking and Communications. IEEE Explore (2012)

    Google Scholar 

  21. Osahon, O., Emmanuel, E.A.: A wireless network infrastructure architecture for rural communities. Int. J. Comput. Sci. Inform. Technol. 9(3), 43–62 (2017)

    Google Scholar 

  22. Johnson, D.L., Pejovic, V., Belding, E.M., Van Stam, G.: Traffic characterization and internet usage in rural Africa. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 493–502. Hyderabad (2011)

    Google Scholar 

  23. van Hoorik, P., Mweetwa, F.: Use of Internet in rural areas of Zambia. In: Proceedings of the IST-Africa Conference, pp. 1–13. Windhoek (2008)

    Google Scholar 

  24. Isaacman, S., Martonosi, M.: Low-infrastructure methods to improve internet access for mobile users in emerging regions. In: Proceedings of the 20th International Conference Companion on World Wide Web, pp. 473–482. Hyderabad (2011)

    Google Scholar 

  25. Alvarion: BreezeMAX 3500: System Manual. Alvarion Homepage. https://www.manualslib.com/manual/1228769. Accessed 14 Jan 2020

  26. Becchi, M., Franklin, M., Crowley, P.: A workload for evaluating deep packet inspection architectures. In: IEEE International Symposium on Workload Characterization, pp. 79–80. Seattle, Washington (2008)

    Google Scholar 

  27. Bujlow, T., Carela-Español, V., Barlet-Ros, P.: Independent comparison of popular DPI tools for traffic classification. Comput. Netw. 76, 75–89 (2015)

    Article  Google Scholar 

  28. Kim, H., Claffy, K.C., Fomenkov, M., Barman, D., Faloutsos, M., Lee, K.: Internet traffic classification demystified: myths, caveats, and the best practices. In: Proceedings of the 4th ACM International Conference on Emerging Networking Experiments and Technologies, pp. 1–12. Madrid (2008)

    Google Scholar 

Download references

Acknowledgements

The research reported in this paper was in part supported by Telkom SA and Infinera SA. We are thankful for their support which enabled successful completion of this work.

Author information

Authors and Affiliations

  1. Walter Sisulu University, Potsdam, East London, 5200, South Africa

    Joshua A. Okuthe

  2. Rhodes University, Drosty Road, Grahamstown, 6139, South Africa

    Alfredo Terzoli

Authors
  1. Joshua A. Okuthe
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Alfredo Terzoli
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Joshua A. Okuthe .

Editor information

Editors and Affiliations

  1. State University of Zanzibar, Zanzibar, Tanzania

    Yahya H. Sheikh

  2. State University of Zanzibar, Zanzibar, Tanzania

    Idris A. Rai

  3. State University of Zanzibar, Zanzibar, Tanzania

    Abubakar D. Bakar

Rights and permissions

Reprints and permissions

Copyright information

© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Okuthe, J.A., Terzoli, A. (2022). Quantifying the Shift in Network Usage Upon Bandwidth Upgrade. In: Sheikh, Y.H., Rai, I.A., Bakar, A.D. (eds) e-Infrastructure and e-Services for Developing Countries. AFRICOMM 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 443. Springer, Cham. https://doi.org/10.1007/978-3-031-06374-9_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06374-9_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06373-2

  • Online ISBN: 978-3-031-06374-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation