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).
The data was divided up by region in order to reduce the number of countries in a single chart. This is done so as to allow separation by eyeball of the IPDV lines for the various countries in a chosen region. It also keeps the data more self-consistent in terms of time zones, development, economy, customs etc. For each region, we show one or more charts of the daily median IPDV. The intervention data is from Wikipedia (e.g. https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Egypt).
We are looking for a significant rise or fall in IPDV possibly correlated with an intervention or growth in cases. The links below point to charts of the IPDV (x-axis in msec.) versus the date. There is no data for 1/30/20 thru 2/2/20 (n.b. 2/1/30-2/2/30 are weekends).
Weekday data:
There are two Excel spreadsheets for the weekday filtered data (two files since the master ran out of tabs):
Alldays data
Since the weekday's data when plotted versus date left gaps that either resulted in gaps in the curves or extended the curve across a weekend thus making the peaks look much wider, it made the data harder to understand by simple visualization. Therefore, we decided to include all days.
Data by host
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 to the number and variety of hosts involved. By variety we mean whether or not they have high-speed Research and Education network links, whether 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
Asia
E Asia
South Asia (to be analyzed)
Africa
Central Africa
East Africa
North Africa
South Africa
West Africa
America
Caribbean
Central America
North America (to be analyzed)
South America
Europe
Baltics
Balkans
Eastern Europe
Northern Europe
Southern Europe
Western Europe
UK and Ireland
Summary
PingER was originally set up for monitoring connections between High Energy Physics (HEP) sites that were mainly in the US, Canada, Europe and Japan. Thus these regions tend to have hosts that are mainly Research or Educational (R&E). As a result, for the above regions, most hosts being R&E have high-performance network links as opposed to public network links. Thus looking at the overall performance for a country in these regions there is little evidence of the impact of Covid-19 interventions, See, for example, the analysis for Spain and Italy, two countries that were badly impacted by Covid-19. Also, see the UK.
On the other hand, almost all hosts monitored in Africa (apart from 3 in the Republic of South Africa), and South America (apart from some in Brazil and Argentina) are on public networks and thus more likely to be impacted by changes in traffic patterns caused by Covid-19 interventions.
The typical impacts are:
- A sustained (for a week or more) significant increase or decrease in IPDV values correlated with an intervention that may be expected to change traffic patterns.
- A change in the difference in the weekday vs the weekend performance (bear in mind all times are UTC) correlated with an intervention.
Future work
Add data for South Asia.
Summarize the success by country in finding hosts with IPDV performance changes correlating with interventions in Africa and South America.