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:
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@ripe.net>
Reply to: Romain Fontugne <romain@iij.ad.jp>
Date: Thursday 2022-06-30 at 07:49
To: Michael Rabinovich <michael.rabinovich@case.edu>, Randy Bush <randy@psg.com>
Cc: "Ponikierski, Grzegorz via ripe-atlas" <ripe-atlas@ripe.net>, Nicholas Kernan <nlk39@case.edu>, Emile Aben <emile.aben@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:
We've also extended this approach to find places where deploying new
Atlas probes would add more diversity to Atlas:
The paper is now available:
Thanks,
Romain