[atlas] Overuse of software probes

Thanks Randy, Michael, Emile and Romain for your data. I will have to allocate some time to go through it.

I think it’s also useful to understand better my specific needs. I usually do anycast measurements per country and I use all probes from the country (yes, I’m even able to use 1000+ probes from DE). I don’t really care about geographical distribution because after all I use all probes in the country. My point is to see if my work goes in good direction and to catch all routing anomalies. That’s why my only concern is to filter out probes which are:


  1.  Definitely not from this country. I regularly report to RIPE team probes which for example are assigned to DE but in reality they are located in GB. I usually catch them when I see that they are routed to anycast node in another country and don’t see domestic hops in traceroutes. Such probes gets system-geoloc-disputed tag so other users can filter out these probes more easily.
  2.  Definitely duplicates of already existing probes (in the same location, in the same AS like in case of Hostinger). They are example that more data is not always good because they skew cumulative results and lead to over representative of given location and ASN.

I hope that data that you shared will help me to make this filtering easier.

Regards,
Grzegorz

From: Romain Fontugne via ripe-atlas <ripe-atlas at ripe.net>
Reply to: Romain Fontugne <romain at iij.ad.jp>
Date: Thursday 2022-06-30 at 07:49
To: Michael Rabinovich <michael.rabinovich at case.edu>, Randy Bush <randy at psg.com>
Cc: "Ponikierski, Grzegorz via ripe-atlas" <ripe-atlas at ripe.net>, Nicholas Kernan <nlk39 at case.edu>, Emile Aben <emile.aben at ripe.net>
Subject: Re: [atlas] Overuse of software probes

On 6/29/22 00:23, Michael Rabinovich wrote:
Looking forward to reading Emile’s paper, but in the meantime: Nick
Kernan, a graduate student of mine, wrote a python script for selecting
a geographically diverse set of probes from a list of probes.

The paper describes a similar approach, but using topological distances
(e.g. AS path length, RTT). It is not perfect but more useful than
Atlas' world-wide probe selection.

Results are weekly updated here:
https://ihr.iijlab.net/ihr/en-us/metis/selection

We've also extended this approach to find places where deploying new
Atlas probes would add more diversity to Atlas:
https://ihr.iijlab.net/ihr/en-us/metis/deployment

The paper is now available:
https://tma.ifip.org/2022/wp-content/uploads/sites/11/2022/06/tma2022-paper18.pdf


Thanks,
Romain

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