Abstract
The first wave of the COVID-19 pandemic hit North America in March 2020, disrupting personal and professional lives, and leading to work-from-home mandates in many jurisdictions. In this paper, we examine two years of empirical network traffic measurement data from the University of Calgary’s campus network to study the effects of the pandemic on a post-secondary education environment. Our study focuses on the online meeting applications and services used, as well as traffic volumes, directionality, and diurnal patterns, as observed from our campus edge network. The main highlights from our study include: changes to inbound and outbound traffic volumes; reduced traffic asymmetry; significant growth in Zoom, Microsoft Teams, and VPN traffic; structural changes in workday traffic patterns; and a more global distribution of campus network users.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
This figure uses the direct information from DAGstats and not the logs captured by Zeek. Therefore, it is not affected by the monitor restarts and the reconfiguration in mid-July. However, it is affected by the monitor crash in late March.
- 2.
We have not analyzed the residence traffic in detail, since the number of users seems low. Ulkani et al. [31] studied pandemic effects on student residence traffic at UCSD, finding changes (for example) in Zoom and OSN usage.
- 3.
Recall that any additional authentication sessions initiated while on campus would not be observable from our monitor.
- 4.
The mid-July configuration change to the monitor restart interval (now 6 h) contributes to the observed increase in connections as well.
- 5.
- 6.
A more detailed analysis shows that some of these are for STUN (Session Traversal Utilities for NAT) protocol traffic on UDP port 19302.
- 7.
For example, a light (green, yellow, or red) on the client’s view to indicate the performance of the Zoom server from the server’s perspective, and possibly tracking over time to summarize the percentage of total meeting time where server performance was green, yellow, or red.
References
Adhikari, V., Guo, Y., Hao, F., Hilt, V., Zhang, Z., Varvello, M., et al.: Measurement study of Netflix, Hulu, and a tale of three CDNs. IEEE/ACM Trans. Networking 23(6), 1984–1997 (2015). https://doi.org/10.1109/TNET.2014.2354262
Amazon Inc., Press Room: AWS and Zoom Extend Strategic Relationship. https://press.aboutamazon.com/news-releases/news-release-details/aws-and-zoom-extend-strategic-relationship. Nov 2020
Arlitt, M., Williamson, C.: Internet web servers: workload characterization and performance implications. IEEE/ACM Trans. Networking 5(5), 631–645 (1997). https://doi.org/10.1109/90.649565
Balliester, T., Elsheikhi, A.: The future of work: a literature review, ILO research department working paper, vol. 29, March 2018
BITAG Technical Working Group: 2020 Pandemic Network Performance (2021). https://www.bitag.org/documents/bitag_report.pdf. Apr 2021
Breslau, L., Cao, P., Fan, L., Phillips, G., Shenker, S.: Web caching and Zipf-like distributions: evidence and implications. In: IEEE INFOCOM 1999. Conference on Computer Communications. Proceedings. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. The Future is Now (Cat. No. 99CH36320), vol. 1, pp. 126–134, March 1999. https://doi.org/10.1109/INFCOM.1999.749260
Chang, H., Varvello, M., Hao, F., Mukherjee, S.: Can you see me now? a measurement study of zoom, Webex, and Meet. In: Proceedings of ACM IMC, pp. 216–228, November 2021. https://doi.org/10.1145/3487552.3487847
Choi, A., Karamollahi, M., Williamson, C., Arlitt, M.: Zoom session quality: a network-level view, to appear. In: Proceedings of Passive and Active Measurement (PAM) Conference, March 2022
Crovella, M., Krishnamurthy, B.: Internet Measurement: Infrastructure, Traffic, and Applications. Wiley & Sons (2006)
Favale, T., Soro, F., Trevisan, M., Drago, I., Mellia, M.: Campus traffic and e-learning during COVID-19 pandemic. Comput. Networks 176, 107290 (2020). https://doi.org/10.1016/j.comnet.2020.107290
Feldmann, A., Gasser, O., Lichtblau, F., Pujol, E., Poese, I., Dietzel, C., et al.: The lockdown effect: implications of the COVID-19 pandemic on internet traffic. In: Proceedings of ACM IMC, pp. 1–18, Pittsburgh, October 2020. https://doi.org/10.1145/3419394.3423658
Fiebig, T., Gürses, S., Gañán, C., Kotkamp, E., Kuipers, F., Lindorfer, M., et al.: Heads in the clouds: measuring the implications of universities migrating to public clouds. arXiv preprint arXiv:2104.09462 (2021)
Gill, P., Arlitt, M., Li, Z., Mahanti, A.: YouTube traffic characterization: a view from the edge. In: Proceedings of ACM IMC, pp. 15–28, San Diego, CA, USA, October 2007. https://doi.org/10.1145/1298306.1298310
Google: COVID-19 community mobility reports (2021). https://www.google.com/covid19/mobility/
Klenow, S., Williamson, C., Arlitt, M., Keshvadi, S.: Campus-level instagram traffic: a case study. In: Proceedings of IEEE MASCOTS, pp. 228–234, Rennes, France, October 2021. https://doi.org/10.1109/MASCOTS.2019.00032
Labovitz, C.: Early effects of COVID-19 lockdowns on service provider networks: the network soldiers on!. https://www.nokia.com/blog/early-effects-covid-19-lockdowns-service-provider-networks-networks-soldier/. Mar 2020
Labovitz, C.: Network traffic insights in the time of COVID-19. https://www.nokia.com/blog/network-traffic-insights-time-covid-19-april-9-update/. 9 Apr 2020
Lamb, A., Fuller, M., Varadarajan, R., Tran, N., Vandier, B., Doshi, L., et al.: The Vertica analytic database: C-Store 7 years later. In: Proceedings of VLDB Endowment, vol. 5, no. 12, pp. 1790–1801, August 2012. https://doi.org/10.14778/2367502.2367518
Liu, S., Schmitt, P., Bronzino, F., Feamster, N.: Characterizing service provider response to the COVID-19 pandemic in the United States. In: Hohlfeld, O., Lutu, A., Levin, D. (eds.) PAM 2021. LNCS, vol. 12671, pp. 20–38. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-72582-2_2
Lutu, A., Perino, D., Bagnulo, M., Frias-Martinez, E., Khangosstar, J.: A characterization of the COVID-19 pandemic impact on a mobile network operator traffic. In: Proceedings of ACM IMC, pp. 19–33, Pittsburg, CA, USA, October 2020
MacMillan, K., Mangla, T., Saxon, J., Feamster, N.: Measuring the performance and network utilization of popular video conferencing applications. In: Proceedings of ACM IMC, pp. 229–244, USA, May 2021. https://doi.org/10.1145/3487552.3487842
Mislove, A., Marcon, M., Gummadi, K., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of ACM IMC, pp. 29–42, New York, NY, USA, October 2007. https://doi.org/10.1145/1298306.1298311
Nagel, L.: The influence of the COVID-19 pandemic on the digital transformation of work. Int. J. Soc. Soc. Policy (2020). https://doi.org/10.1108/IJSSP-07-2020-0323
Paxson, V.: Bro: a system for detecting network intruders in real-time. Comput. Netw. 31(23), 2435–2463 (1999). https://doi.org/10.1016/S1389-1286(99)00112-7
Paxson, V.: Empirically derived analytic models of wide-area TCP connections. IEEE/ACM Trans. Networking 2(4), 316–336 (1994). https://doi.org/10.1109/90.330413
Porpiglia, F., Checcucci, E., Autorino, R.: Traditional and virtual congress meetings during the COVID-19 pandemic and the post-COVID-19 Era: is it time to change the paradigm? Eur. Urol. 78(3), 301–303 (2020). https://doi.org/10.1016/j.eururo.2020.04.018
Pratama, H., Azman, M., Kassymova, G., Duisenbayeva, S.: The trend in using online meeting applications for learning during the period of pandemic COVID-19: a literature review. J. Innovation Educ. Cult. Res. 1(2), 58–68 (2020). https://doi.org/10.1007/978-3-030-40716-2_2
Ramachandran, A., Feamster, N.: The trend in using online meeting applications for learning during the period of pandemic COVID-19: a literature review. In: Proceedings of ACM SIGCOMM, New York, NY, USA, pp. 291–302, August 2006. https://doi.org/10.1145/1159913.1159947
Santana, M., Cobo, M.: What is the future of work? Sci. Mapp. Anal. Eur. Manage. J. 38(6), 846–862 (2020). https://doi.org/10.1016/j.emj.2020.04.010
Schneider, F., Feldmann, A., Krishnamurthy, B., Willinger, W.: Understanding online social network usage from a network perspective. In: Proceedings of ACM IMC, pp. 35–48, Chicago, IL, USA, November 2009. https://doi.org/10.1145/1644893.1644899
Ukani, A., Mirian, A., Snoeren, A.: Locked-in during lock-down: undergraduate life on the internet in a pandemic. In: Proceedings of ACM IMC, pp. 480–486, November 2021. https://doi.org/10.1145/3487552.3487828
Acknowledgements
The authors thank the PAM 2022 reviewers and shepherd Tobias Fiebig for their constructive suggestions that helped to improve our paper. The authors are also grateful to UCIT for facilitating our collection of campus-level network traffic data, and to the team at ARC for technical support in the storage and management of our data. Financial support for this research was provided by Canada’s Natural Sciences and Engineering Research Council (NSERC).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Appendix
Appendix
The monitor reconfiguration mentioned earlier happened in the course of a week. On July 6, 2020, we changed the reset interval from one hour to every three hours to test the robustness of the monitor against the large volume of scanning activity and how disabling the scanning module is effective. The experiment was successful, and on July 13, 2020, we again changed the reset interval to every six hours. We then settled with that interval as our subsequent resource monitoring suggested that a longer interval may cause problems.
Distributions of connection durations during five working days of June 29, 2020 to July 3, 2020 (before monitor reconfiguration) and five working days of July 13, 2020 to July 17, 2020 (after monitor reconfiguration).
Figure 14 shows the distribution of connection durations for five working days from June 29 to July 3, 2020 (representing before reconfiguration) and another five working days from July 13 to July 17, 2020 (representing after reconfiguration). Both distributions follow a very similar pattern, with the post-reconfiguration graph stretching slightly to the right and longer tail on the LLCD plot, showing a heavier tail for the distribution that attributes to the connections lasting between 1 to 6 h (note the log2-based x-axis). However, the most significant difference between these distributions (not evident in this figure) is that more than 615 million connections were captured during these post-reconfiguration days, while this number for the pre-reconfiguration days was more than 570 million. There is about 45 million difference between the number of connections in these distributions, out of which only about 574 thousand lasted between 1 to 6 h. It shows that the reconfiguration not only helped in capturing longer connections (which is very impactful for some applications, such as Zoom and VPN) but also more connections in general, due to fewer restarts per day.
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Karamollahi, M., Williamson, C., Arlitt, M. (2022). Zoomiversity: A Case Study of Pandemic Effects on Post-secondary Teaching and Learning. In: Hohlfeld, O., Moura, G., Pelsser, C. (eds) Passive and Active Measurement. PAM 2022. Lecture Notes in Computer Science, vol 13210. Springer, Cham. https://doi.org/10.1007/978-3-030-98785-5_26
Download citation
DOI: https://doi.org/10.1007/978-3-030-98785-5_26
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98784-8
Online ISBN: 978-3-030-98785-5
eBook Packages: Computer ScienceComputer Science (R0)