Introduction

This addresses the question of whether the PingER data can identify the impact of COVID-19 on the Internet. According to https://thrivenextgen.com/covid-19-impact-on-internet-performance/ Most Internet Service Providers (ISPs) oversubscribe their bandwidth and networks as typical inbound and outbound traffic are bursty and often don’t sustain high levels on a continuous basis. Fortunately, most of the larger National Providers have had sufficient time to prepare for the impact of COVID-19 and plan for the possibility of business workloads shifting to the home. Regardless of this capacity planning, Internet traffic patterns are about to change drastically based on schools across the country opting for virtual learning and business work from home migrations. Also, Time Magazine April 5/ April 15, 2020, reports that "traffic worldwide is up 35%...Demand is highest in the evening in the past two weeks, says networking firm Century Link."

For example, one might expect that as schools shut, people self-isolate, are sent home from, work from home, lockdowns are imposed etc., physical person-to-person communications would migrate to the Internet and will increase the use of the Internet e.g. by virtual learning, streaming. communicating, gathering information and entertaining for example.  The thought is that this will lead to different Internet patterns and possibly lead to Internet congestion. A study of the impact of COVID-19 on Internet speeds by Ookla  ("TRACKING COVID-19’S IMPACT ON GLOBAL INTERNET PERFORMANCE") is directly relevant, however,  it is hard to identify any dramatic impact on the latencies in the report. There is also COVID-19 impacts on Internet traffic: Seattle, Northern Italy, and South Korea which looks at the impact on Internet traffic volumes. Also companies such as Netflix, Amazon, Apple, Disney, Google have reduced the quality of their videos to help reduce traffic and congestion of the Internet (see https://www.traffic-masters.net/covid19-web-traffic-statistics/).

Using PingER data and comparing the various metrics it provides including: average Round Trip Time (RTT), Conditional Loss Probability (CLP). Inter Packet Delay Variability IPDV), Inter Quartile Range (IQR) of the round trip times, and derived throughput (see Tutorial on Internet Monitoring and Pinger at SLAC); we ascertained that the most stable and yet sensitive metric in detecting changes caused by  Covid-19 interventions was the Inter Packet Delay Variation (IPDV). The data analyzed are for 120 days starting around mid to late January 16th and ending around mid to late May  2020. For each country, there is 1 point per day. Initially only weekday data was used in order to reduce the variability of the data and to focus more on the effect of interventions such as closing the workplaces, schools, universities, people working from home, or being out of work. The weekday was determined based on the Universal Time  Coordinated (UTC).

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Weekday data:

There are two Excel spread sheets spreadsheets for the weekday filtered data (two files since the master ran out of tabs): 

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The analysis with the data aggregated by the country was generally unsuccessful (see here) in identifying changes in performance correlated with the interventions. This is partially due number and variety of hosts involved. By variety we mean whether or not they have high-speed Research and Education network links, whether they the sites are educational, commercial, etc. We, therefore, decided to study the impact of interventions in more detail by reviewing the data within a country by host. This enabled fewer trajectories to track by eye, and also the separation of R&E and educational links from others. The results below are for all days and by host.

Results by region, country and by host

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