Typhoon Haiyan - What we see in RIPEstat and RIPE Atlas
As one of the strongest typhoons on record, Haiyan is feared to have a devastating effect on the Philippines. We looked for evidence of its effects on the country's core Internet infrastructure. At first glance, the typhoon's impact on core Internet infrastructure appears to be modest. This is in …
Code for looking into AS Adjacency changes is available here: https://github.com/emileaben/as-neighbour-diff
Code on how to create graphs like Figure 1 ( ie. BGP view of how networks in a country interconnect ) is available here: https://github.com/InternetHealthReport/country-as-hegemony-viz
NOG Alliance is helping out network operators in Ukraine: https://nogalliance.org/our-task-forces/keep-ukraine-connected/
An effort related to keeping Urkanian servers/websites online by the Dutch Cloud Community: https://dutchcloudcommunity.nl/community/cloud4ukraine/
We got a request for the HHI scores for other countries. I've put these in a small repo on github together with the code that generated this. repo: https://github.com/emileaben/hhi-eyeballs HHI scores for 2022-03-07 are available here: https://raw.githubusercontent.com/emileaben/hhi-eyeballs/main/eyeball-hhi.2022-03-07.csv
“This is awesome work, thank you! Do you maybe have the script/notebooks/sources to reproduce this? This could be potential used for other countries.”
Hi Jenneth, The observable notebook we used for this is here: https://observablehq.com/@aguformoso/internet-outages-as-seen-by-ripe-atlas . It's a little rough around the edges, so it would be great if you could help improve it!
Thanks for your comment Maxime. I would love to see more analysis too, and the tool allows people to do this. Take for instance this thread on Twitter where Jason Livingood analyses the signals for the US: https://twitter.com/jlivingood/status/1245142990336688130 If others have analysis for specific countries they want to share it would be great to have them collected, for instance as comments to this RIPE Labs post!
“One may assume that, if some people delayed the changes, other people rushed in to adapt the networks to the increased load? Both behaviour may explain why the change rate is more or less the same?”
I think it would be interesting to dig into this data deeper indeed. I looked at splitting this out per country a bit, but could try figure out if there are trends in the sets of ASNs in this timeseries. Would you be willing to look at this? My colleague Vesna is doing a virtual hackathon around Internet and Corona ( https://labs.ripe.net/Members/becha/hackathons-in-the-time-of-corona ), I'd love it if we could collaborate around this. Let me or Vesna know, or hop on to the conf calls, Mon 2pm UTC ( 3pm Paris timezone :) )
“Ghost routes: https://www.sixxs.net/tools/grh/what/”
I've added a reference to the different names 'stuck routes' and 'ghost routes' for this phenomenon at the beginning of the post. Thanks for the pointer!
“Interested in repeating this analysis for 2018 world cup?”
Hi Dan, we have no plans of repeating this analysis this time. This type of signal is still there, see for instance https://twitter.com/search?q=%40ohohlfeld%20%23worldcup&src=typd for a couple of graphs that show the impact in various places.
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