Introduction
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 spread sheets 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 country was generally unsuccessful in identifying changes in performance correlated with the interventions. We therefore decided to study the impact of interventions in more detail by reviewing the data within a country by host.
Results by region, country and by host
Asia
E Asia
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. As a result, for the above regions most hosts are Research and Advanced Education and 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) are on public networks and thus more likely to be impacted by changes in traffic patterns caused by Covid-19 interventions.
Possible Future work
Compare weekday usage with allweek usage to see if it makes a notable difference in identifying correlations between PingER and Covid-19. DONE
Rather than use weekdays use midweek (Tue -Thu) to ensure the data avoids weekends. DONE
For each country select the type of target based on:
- is the connection to SLAC the connection a lightly loaded high-performance link (e.g. using ESnet, Internet2, DANTE, TENET etc),
- is the target host in an educational, commercial, government, organization, or unknown establishment.