Follow the same directions until the get-measurements step. This version will create a traixroute.json file inside the working directory, that will have to be used as input for traIXroute (be wary: traixroute.json should be on the same directory with the results folder). You can get a ixp-country-jedi-able traIXroute version at:
https://github.com/slowr/traIXroute
The instructions for traIXroute are in its README.md
After traIXroute completes, continue with the original IXP-Country-Jedi steps.
Probe mesh measurements
This codebase contains a couple of scripts to make probe mesh measurements feasible/easy.
The way the scripts are used/tested is first create a directory for your measurement campaign, and run the scripts
mkdir 'SE-2015-03'
cd SE-2015-03
<create config.json file here>
<run scripts relative to current directory, ie. ../prepare.py ;
../measure.py ...>
Scripts depend on this convention.
Dependencies on external python modules are in 'requirements.txt', so you can run
pip install -r requirements.txt
to fulfill these.
Before you use the scripts, create a config file specifying what mesh you'd want to do.
This file contains base data used for creating measurements and analysing the results. It currently contains 3 parts:
*country*: ISO 2 letter code for the country under analysis,
or list of countries under analysis.
*ixps*: List of IXPs one wants to detect/report on
*locations*: List of cities on which probe selection will be
based.
An example is provided in the example directory. Only the country is mandatory. Without a locations section, the capital of country (or first country if country is a list) is used for probe selection (see below).
One can set an extra 'location-constraint' config key. The value of this configuration directive needs to be an integer. When this is set, the probe selection part of the ixp-country-jedi will only select probes that are within 'location-constraint' kilometers from any of the given locations. This can be useful if you only want to measure a specific city. In that case set the 'location'-list to that specific city, and set a 'location-constraint' to a reasonable value (for instance 50, so you'll cover 50 kilometers from the city centre).
This script reads config.json and spits out 2 other files basedata.json and probeset.json
For each of the ixps listed in config.json it will try to find the member ASNs
Based on the country and locations specified it will do a probe selection:
- only select public probes (because we want to measure to their
public IP address)
- per ASN will select up to 2 times the number of locations
specified
If an ASN hosts more probes, the selection will be a maximum of 2x the number of locations in config.json: the closest as well as the furthest probe from each location will be selected. Note that this can result in less then 2 times the number-of-locations probes per ASN even when there are more probes available in that ASN, because a single probe could be the furthest away from multiple locations.
In the preparation/datagathering phase this script uses the GeoNames service for geocoding. Please put a valid geonames username in ~/.geonames/auth , for more info on GeoNames see http://www.geonames.org/export/web-services.html
This script does one-off measurements for the probes specified in probeset.json and stores the results of that in measurementset.json
This uses the RIPE Atlas measurement API for measurement creation, and it needs a valid measurement creation API key in ~/.atlas/auth . For more information on RIPE Atlas API keys see https://atlas.ripe.net/docs/keys/
This script gathers metadata for all IPs in the collected data. This is done separately from the rest of analysis-code because it is time-consuming and ideally is done pretty soon after making all measurements. If done too soon, not all measurement results are in yet, if done much later (think days) meta-data, like reverse hostname mapping might have changed, so ballpark is don't get-ips a few minutes after measure.py. This step will create an 'ips.json-fragments' file with reverse DNS lookups, ASNs and geoloc (via OpenIPMap, not MaxMind) of IPs encountered in traceroutes.
This script fetches measurements (from measurementset.json) and does some initial analyses on them using information from config.json, basedata.json, probeset.json and ips.json-fragments. It creates a local results directory and outputs a single json file per measurement (analysed.<msm_id>.json) which is a list of analysis results, one result per src/dst combination.
This script produces text and/or webpages with analysis and visualisation in a local analysis directory. For webpages these need to be on an actual webserver for some of the javascript in them to work. One can easily create a local webserver that would work for this purpose like this:
cd analysis ; python -m SimpleHTTPServer 3333
and then pointing your browser at localhost:3333/ Note that some visualisations use libraries in a common directory located in the 'analysis' directory, so the webserver needs to run with the 'analysis'-directory (or lower) as root.
Templates (HTML,javascript,CSS) for webpages are in the 'templates' directory and copied over each time the analyse-results.py script is run, If you want to tweak the visualisations, tweaking them in the 'templates' directory and then running the analyse-results.py script again will probably do what you want.