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Re: [tor-bugs] #3260 [Analysis]: Learn client speed trends by evaluating directory request download times
#3260: Learn client speed trends by evaluating directory request download times
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Reporter: karsten | Owner: karsten
Type: task | Status: assigned
Priority: normal | Milestone:
Component: Analysis | Version:
Keywords: performance bootstrap | Parent:
Points: | Actualpoints:
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Comment(by karsten):
Replying to [comment:1 arma]:
> I'd suggest the next step is to figure out why it's too hard for
somebody else to grab the data and do the analysis. Learning that lesson
might help us get more analysts on board.
A fine question. The analysis question (this ticket) was on Trac for 10
months without anybody picking it up. The analysis above is based on data
specified in [https://gitweb.torproject.org/torspec.git/blob/HEAD:/dir-
spec.txt#l744 dir-spec.txt], explained on the
[https://metrics.torproject.org/formats.html#dirreqstats Formats page],
and publicly available on the
[https://metrics.torproject.org/data.html#relaydesc Data page]. My parser
class has 150 rather quickly written lines of
[https://gitweb.torproject.org/metrics-
tasks.git/blob/e435e80:/task-3260/ExtractClientSpeedTrends.java Java code]
and uses the [https://gitweb.torproject.org/metrics-lib.git Java metrics
descriptor library] to do the parsing; a simple Python program would have
worked, too. The [https://gitweb.torproject.org/metrics-
tasks.git/blob/e435e80:/task-3260/client-speed-trends.R plotting code] is
standard R and ggplot2; this could have been done with gnuplot et al.,
too. Coding, testing, and analyzing took me 7 hours, parsing took 5 hours
and maxed out I/O on my Core 2 Duo with 8 GB RAM.
So, what do you think is the resource that's least accessible to potential
analysts?
--
Ticket URL: <https://trac.torproject.org/projects/tor/ticket/3260#comment:6>
Tor Bug Tracker & Wiki <https://trac.torproject.org/>
The Tor Project: anonymity online
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