Quantifying University Network Frustrations

September 9, 2010

python obsolete

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I’m sitting in class frustrated as could be. The Internet in this room is unbelievably annoying. For some reason, everything runs fine, then functionality drops to unusable levels. Downloading files (i.e., PDFs of lectures) occurs at about 0.5kb/s (wow), and Internet browsing is hopeless. At most, I can connect to IRC and enjoy myself in #electronics, #python, and #linux. I decided to channel my frustration into productivity, and wrote a quick Python script to let me visualize the problem.

Notice the massive lag spikes around the time class begins. I think it’s caused by the retarded behavior of windows update and anti-virus software updates being downloaded on a gazillion computers all at the same time which are required to connect to the network on Windows machines. Class start times were 8:30am, 9:35am, and 10:40am. Let’s view it on a logarithmic scale:

Finally, the code. It’s two scripts:

This script pings a website (kernel.org) every few seconds and records the ping time to “pings.txt”:

import socket
import time
import os
import sys
import re


def getping():
    pingaling = os.popen("ping -q -c2 kernel.org")
    sys.stdout.flush()
    while 1:
        line = pingaling.readline()
        if not line:
            break
        line = line.split("n")
        for part in line:
            if "rtt" in part:
                part = part.split(" = ")[1]
                part = part.split('/')[1]
                print part+"ms"
                return part


def add2log(stuff):
    f = open("pings.txt", 'a')
    f.write(stuff+",")
    f.close()


while 1:
    print "pinging...",
    stuff = "[%s,%s]" % (time.time(), getping())
    print stuff
    add2log(stuff)
    time.sleep(1)

This script graphs the results:

import pylab
import time
import datetime
import numpy


def smoothTriangle(data, degree, dropVals=False):
    triangle = numpy.array(range(degree)+[degree]+range(degree)[::-1])+1
    smoothed = []
    for i in range(degree, len(data)-degree*2):
        point = data[i:i+len(triangle)]*triangle
        smoothed.append(sum(point)/sum(triangle))
    if dropVals:
        print "smoothlen:", len(smoothed)
        return smoothed
    while len(smoothed) < len(data):
        smoothed = [None]+smoothed+[None]
    if len(smoothed) > len(data):
        smoothed.pop(-1)
    return smoothed


print "reading"
f = open("pings.txt")
raw = eval("[%s]" % f.read())
f.close()

xs, ys, big = [], [], []
for item in raw:
    t = datetime.datetime.fromtimestamp(item[0])
    maxping = 20000
    if item[1] > maxping or item[1] == None:
        item[1] = maxping
        big.append(t)
    ys.append(float(item[1]))
    xs.append(t)

print "plotting"
fig = pylab.figure(figsize=(10, 7))
pylab.plot(xs, ys, 'k.', alpha=.1)
pylab.plot(xs, ys, 'k-', alpha=.1)
pylab.plot(xs, smoothTriangle(ys, 15), 'b-')
pylab.grid(alpha=.3)
pylab.axis([None, None, None, 2000])
pylab.ylabel("latency (ping kernel.org, ms)")
pylab.title("D3-3 Network Responsiveness")
fig.autofmt_xdate()
pylab.savefig('out.png')
pylab.semilogy()
pylab.savefig('out2.png')
fig.autofmt_xdate()
print "done"

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