On March 14, 2024, we observed a large outage in several West African countries. In Ivory Coast and Liberia, the outage was quite severe, affecting 93% of the active network blocks:
Fortunately some locations were able to partially recover from the problems, presumably by routing through different paths:
The root cause for these outages is likely a problems in multiple undersea telecommunication cables, as has been reported in the Washington Post and the Guardian, among other places.
On November 4th, 2023, we observed a large, 4-hour Internet outage around El Paso, Texas:
News reports indicate that Spectrum had a cable cut.
Trinocular showed an outage from 8:40am to 1:05pm (mountain time zone), with an smaller initial outage starting at 7am (2023-11-04t15:40 to t20:00 UTC, possibly starting at 14:00 UTC). This outage was quite severe, affecting more than 40% of the local networks that we monitor.
Cable cuts are hard to deal with, and we’re happy that they were able to restore service relatively quickly!
We’ve been watching Hurricane Ian move across the Gulf of Mexico as it approached Florida. It looks like it made landfall about 3pm EDT (19:00Z).
Our Trinocular Internet outage detection showed the first residential Internet outages starting around 2:20pm EDT (18:22Z) and they go up from there. Our most recent data (as of this post at 7:45pm EDT, or 23:45Z) shows outages from about 75 minutes ago at 6:15pm EDT (22:13Z) with network outages over 50% in most of the western peninsula.
We hope folks stay inside and safe!
News reports (for example, at the Verge and Slashdot) mention a large outage in Rogers, a major Canadian telecommunications provider.
We see lots of evidence for this in our Internet outage detection system.
It’s big! Maybe 30% of Toronto and southern Ontario networks, plus a lot of outages in New Brunswick.
Ontario:
New Brunswick:
An update: Newfoundland also sees a lot of outages. Quebec looks in pretty good shape, though.
And it’s lasting a long time. It looks like it started at 5am Eastern time (2022-07-08t09:00Z), it it has lasted 9.5 hours so far!
We wish Rogers personnel and our Canadian neighbors the best.
Update at 2022-07-09t06:15Z (2:15am Eastern time): Toronto is doing much better, with “only” 10% of blocks unreachable (22808 of 21.5k in the 43.8N,79.3W 0.5 grid cell). New Brunswick and Newfoundland still look the same, with outages in about 50% of blocks.
Update at 2022-07-09t21:10Z (5:10pm Eastern time): It looks like many Rogers networks recovered at 2022-07-09t05:15Z (1:15am Eastern time). This includes all of New Brunswick and Newfoundland and most of Ontario. Trinocular has about a one-hour delay while it computes results, so I did not see this result when I checked in the prior update–I needed to wait 15 minutes more.
We published a new paper “Visualizing Internet Measurements of Covid-19 Work-from-Home” by Erica Stutz (Swarthmore College), Yuri Pradkin, Xiao Song, and John Heidemann (USC/ISI) at the Symposium for REU Research in Data Science, Systems, and Security, co-located with IEEE BigData 2021.
From the abstract:
The Covid-19 pandemic disrupted the world as businesses and schools shifted to work-from-home (WFH), and comprehensive maps have helped visualize how those policies changed over time and in different places. We recently developed algorithms that infer the onset of WFH based on changes in observed Internet usage. Measurements of WFH are important to evaluate how effectively policies are implemented and followed, or to confirm policies in countries with less transparent journalism.This paper describes a web-based visualization system for measurements of Covid-19-induced WFH. We build on a web-based world map, showing a geographic grid of observations about WFH. We extend typical map interaction (zoom and pan, plus animation over time) with two new forms of pop-up information that allow users to drill-down to investigate our underlying data.We use sparklines to show changes over the first 6 months of 2020 for a given location, supporting identification and navigation to hot spots. Alternatively, users can report particular networks (Internet Service Providers) that show WFH on a given day.We show that these tools help us relate our observations to news reports of Covid-19-induced changes and, in some cases, lockdowns due to other causes. Our visualization is publicly available at https://covid.ant.isi.edu, as is our underlying data.
Datasets from this work will be available from our website and can be seen now at https://covid.ant.isi.edu. We thank NSF grants 2028279 and CNS-2007106 for supporting this work.
We are happy to announce a new project “Measuring the Internet during Novel Coronavirus to Evaluate Quarantine” (MINCEQ).
Measuring the Internet during Novel Coronavirus to Evaluate Quarantine (RAPID-MINCEQ) is a project to measure changes in Internet use during the COVID-19 outbreak of 2020. As the world grapples with COVID-19, work-from-home and study-from-home are widely employed. Implementation of these policies varies across the U.S. and globally due to local circumstances. A common consequence is a huge shift in Internet use, with schools and workplaces emptying and home Internet use increasing. The goal of this project is to observe this shift, globally, through changes in Internet address usage, allowing observation of early reactions to COVID and, one hopes, a future shift back.
This project plans to develop two complementary methods of assessing Internet use by measuring address activity and how it changes relative to historical trends. The project will directly measure Internet address use globally based on continuous, ongoing measurements of more than 4 million IPv4 networks. The project will also directly measure Internet address use in network traffic at a regional Internet exchange point where multiple Internet providers interconnect. The first approach provides a global picture, while the second provides a more detailed but regional picture; together they will help evaluate measurement accuracy.
The project website is at https://ant.isi.edu/minceq/index.html. The PI is John Heidemann. This work is supported by NSF as a RAPID award in response to COVID-19, award NSF-2028279.