“Technical Perspective: Tracking Pandemic-Driven Internet Traffic”
Communications of the ACM, July 2021, Vol. 64 No. 7, Page 100
Research Highlights
By Jennifer Rexford
(Yes, this article is related to “A Year in Lockdown: How the Waves of COVID-19 Impact Internet Traffic.” Keep reading…)
“As challenging as the past year (and more) has been, the Internet has made it possible for many important aspects of life, work, and culture to continue.”
The Internet is a research experiment that “escaped from the lab” to become a critical global communications infrastructure during our lifetimes. Over the past year of the COVID-19 pandemic, the Internet has supported friends and families staying in touch and supporting each other, remote work and learning, and the global collaboration of experts designing much-needed treatments and vaccines. As challenging as the past year (and more) has been, the Internet has made it possible for many important aspects of life, work, and culture to continue.
In March 2020, the Internet suddenly became a lifeline for people all over the world. Designed to withstand failures, attacks, and fluctuations in traffic, the Internet proved up to the task. Almost overnight, demand for Internet services grew dramatically, and shifted in both time and space. Many Internet service providers (ISPs) had network designs with spare capacity, deployed more bandwidth in critical locations, and relaxed bandwidth caps on low-income households. The Internet protocols, designed to adapt to changing conditions, were able to deliver reasonable service to many users by sharing the available resources dynamically.
The following paper offers a detailed look at how Internet traffic changed during the COVID-19 pandemic. The paper is distinctive in analyzing traffic measurements from multiple networks—ISPs, three major Internet eXchange points (IXPs), a mobile provider, and a university network—across a long period of time. The combination of longitudinal data from multiple, diverse vantage points is truly unusual, and a testament to the large group of authors who worked with each other, their home institutions, and other stakeholders to acquire the measurement data.
About the Author:
Jennifer Rexford is the Gordon Y.S. Wu Professor in Engineering and chair of the Computer Science Department at Princeton University, Princeton, NJ, USA.
“A Year in Lockdown: How the Waves of COVID-19 Impact Internet Traffic”
Communications of the ACM, July 2021, Vol. 64 No. 7, Pages 101-108
Research Highlights
By Anja Feldmann, Oliver Gasser, Franziska Lichtblau, Enric Pujol, Ingmar Poese, Christoph Dietzel, Daniel Wagner, Matthias Wichtlhuber, Juan Tapiador, Narseo Vallina-Rodriguez, Oliver Hohlfeld, Georgios Smaragdakis
In March 2020, the World Health Organization declared the Corona Virus 2019 (COVID-19) outbreak a global pandemic. As a result, billions of people were either encouraged or forced by their governments to stay home to reduce the spread of the virus. This caused many to turn to the Internet for work, education, social interaction, and entertainment. With the Internet demand rising at an unprecedented rate, the question of whether the Internet could sustain this additional load emerged. To answer this question, this paper will review the impact of the first year of the COVID-19 pandemic on Internet traffic in order to analyze its performance. In order to keep our study broad, we collect and analyze Internet traffic data from multiple locations at the core and edge of the Internet. From this, we characterize how traffic and application demands change, to describe the “new normal,” and explain how the Internet reacted during these unprecedented times.
1. Introduction
The worldwide pandemic caused by the Corona Virus 2019 (COVID-19) is a once-in-a-generation global phenomenon that changed the lives of billions of people and destabilized the interconnected world economy. What started as a local health emergency in Asia at the end of 2019, turned into a global event at the beginning of 2020 when the first cases appeared on other continents. By March 2020, the World Health Organization (WHO) declared COVID-19 as a pandemic, causing many governments around the globe to impose strict lockdowns of economic and social activities to reduce the spread of COVID-19. These measures changed the habits of a large fraction of the global population, who now depend on residential Internet connectivity for work, education, social interaction, and entertainment.
Changes in Internet user behavior are common, but they normally occur gradually and over long periods of time. Notable examples of such changes are the increase in demand for peer-to-peer applications that happened in the early 2000s; the increase of traffic served by content delivery networks—such as an increase in streaming—that took place in the 2010s; and, more recently, the elevated demand for mobile applications. In all of these cases, the telecommunications industry and network operator community reacted by increasing the investment on network infrastructure. However, the changes in Internet user behavior during the pandemic have been unique because the shifts took place within weeks, leaving hardly any time to react. This raised questions of whether user behavior changes yield to changes in Internet traffic and, more importantly, concerns if the Internet is able to sustain this additional load.
In this paper, we investigate the impact of the COVID-19 pandemic on the Internet traffic by analyzing more than two years of Internet traffic data including the first year of the pandemic. More specifically, we characterize the overall traffic shifts and the changes in demand for particular applications that became very popular in a short amount of time. During the process, we try to understand if there is a “new normal” in Internet traffic and to see how the Internet reacted in these unprecedented times. We summarize our observations for the spring 2020 wave (February 2020 to June 2020) and then extend our study for the falla 2020 wave (September 2020 to February 2021). To that end, we collect and analyze network traffic data from multiple vantage points, such as a large Internet Service Provider (ISP) in Europe, three Internet Exchange Points (IXPs) in Europe and the US, as well as a mobile operator and a metropolitan academic network in Europe (REDIMadrid).
Our main observations can be summarized as follows:
- Changes in traffic volume follow demand changes, causing a traffic surge of 15–20% during the fall 2020 lockdown for the ISP/IXPs in our study. In summer 2020, after the reopening of the economy, an increase of about 20% at one IXP, but only 6% at the Tier-1 ISP, is still visible. The fall 2020 wave also had an impact, with the annual traffic increase in 2020 being higher than in a typical year.
- The observed traffic increase mostly takes place during nontraditional peak hours. Daily traffic patterns are moving to weekend-like patterns, especially during the spring 2020 lockdown.
- Traffic related to remote working applications, such as VPN connectivity applications and video-conferencing applications, surges by more than 200%. VPN traffic seems to remain at elevated levels even during the fall 2020 wave.
- Traffic changes across networks differ. For example, in the REDIMadrid campus network, there was a significant drop (by up to 55%) in traffic volume on workdays after the spring 2020 lockdown as most people were not on campus, but an increase during the fall 2020 lockdown. Traffic at the IXP and the ISP also varies depending on the mandated lockdown policy and due to the different customer profiles.
About the Authors:
Anja Feldmann, Max Planck Institute for Informatics, Saarbrücken, Germany.
Oliver Gasser, Max Planck Institute for Informatics, Saarbrücken, Germany.
Franziska Lichtblau, Max Planck Institute for Informatics, Saarbrücken, Germany.
Enric Pujol, BENOCS, Berlin, Germany.
Ingmar Poese, BENOCS, Berlin, Germany.
Christoph Dietzel, DE-CIX, Cologne, Germany and Max Planck Institute for Informatics, Saarbrücken, Germany.
Daniel Wagner, DE-CIX, Cologne, Germany and Max Planck Institute for Informatics, Saarbrücken, Germany.
Matthias Wichtlhuber, DE-CIX, Cologne, Germany.
Juan Tapiador, Universidad Carlos III de Madrid, Madrid, Spain.
Narseo Vallina-Rodriguez, IMDEA Networks, Madrid, Spain and ICSI, Berkeley, USA.
Oliver Hohlfeld, Brandenburg University of Technology, Cottbus, Germany.
Georgios Smaragdakis, TU Berlin, Berlin Institute for the Foundations of Learning and Data, Berlin, Germany and Max Planck Institute for Informatics, Saarbrücken, Germany.
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