Introduction
The PingER Metrics Intensity Mapsare intensity maps of the Internet performance as measured by PingER. Having gathered data since 1998, the charts enable the users to study the trends and significant improvements as well as degradations. The different sets of charts (w.r.t. regions) enables the users to study the progress made by countries in comparison to their neighbours as well as the world in general.
These charts were made from the Google Visualization API for intensity maps.
Below are some examples of observations made from Stanford Linear Accelerator Center (SLAC), USA:
Average RTT (ms) |
Minimum RTT (ms) |
Normalized Throughput (Kbps) |
Unreachability (%) |
|---|---|---|---|
|
|
|
|
Interpretation
Each of the intensity maps (also known as heat maps) present a single metric as observed by PingER monitoring nodes. Each country is coloured with respect to the measured performance by selecting colours from the gradient defined at the bottom. The measurement at the left bottom (white) defines the minimum value observed, where as the measurement at the right bottom (solid colour) defines the maximum value observed. The tabs are labeled with the metric name along with their units.
Implementation Details
HTML output
To create the intensity maps, the data must be loaded in a data structure (as explained by the Google Visualization API and) shown below:
<html>
<head>
<script type="text/javascript" src="http://www.google.com/jsapi"></script>
<script type="text/javascript">
google.load("visualization", "1", {packages:["intensitymap"]});
google.setOnLoadCallback(drawChart);
function drawChart() {
var data = new google.visualization.DataTable();
data.addColumn('string', '', 'Country');
data.addColumn('number', 'Average RTT (ms)', 'a');
data.addColumn('number', 'Normalized Throughput (Kbps)', 'b');
data.addColumn('number', 'Internet Users (#)', 'c');
data.addColumn('number', 'Internet Penetration (%)', 'd');
data.addColumn('number', 'Minimum RTT (ms)', 'e');
data.addColumn('number', 'Packet Loss (%)', 'f');
data.addColumn('number', 'Unreachability (%)', 'g');
data.addColumn('number', 'Jitter (ms)', 'h');
data.addRows(3);
data.setValue(0, 0, "AF");
data.setValue(0, 1, 743.925);
data.setValue(0, 2, 2029.71824405769);
data.setValue(0, 3, 25000);
data.setValue(0, 4, 0.1);
data.setValue(0, 5, 690.421);
data.setValue(0, 6, 1.115);
data.setValue(0, 7, 6.217);
data.setValue(0, 8, 12.922);
data.setValue(1, 0, "DZ");
data.setValue(1, 1, 251.264);
data.setValue(1, 2, 1818.78324078846);
data.setValue(1, 3, 845000);
data.setValue(1, 4, 2.6);
data.setValue(1, 5, 208.571);
data.setValue(1, 6, 1.103);
data.setValue(1, 7, 8.006);
data.setValue(1, 8, 4.764);
data.setValue(2, 0, "AD");
data.setValue(2, 1, 167.507);
data.setValue(2, 2, 11732.2839646154);
data.setValue(2, 3, 24500);
data.setValue(2, 4, 35.7);
data.setValue(2, 5, 158.672);
data.setValue(2, 6, 0.033);
data.setValue(2, 7, 1.734);
data.setValue(2, 8, 0.765);
var chart = new google.visualization.IntensityMap(document.getElementById('chart_div'));
chart.draw(data, {});
}
</script>
</head>
<body>
<div id="chart_div"></div>
</body>
</html>
The data must comply with the formatting requirements (as stated by Google Visualization API and copied below):
- The first column should be a string, and contain country ISO 3166-1-alpha-2 codes.
- Any number of columns can follow, all must be numeric. Note that each numerical column defines a separate tab on the chart.
Relevant files
Following are the files involved in the process of generating the HTML page:
Application:
File path: /afs/slac/package/pinger/intensity-map/create-intensity-map-page.pl trscrontab entry: There is none
The script that defines the input arguments to generate the relevant .html files:
File path: /afs/slac/package/pinger/intensity-map/generate-intensity-maps.pl trscrontab entry: pinger 2 2 * * * /afs/slac/package/pinger/intensity-map/generate-intensity-maps.pl \ > /afs/slac/package/pinger/intensity-map/generate-intensity-maps.stdout #Takes < 1 min OK on RHEL6
The ISO 3166-1-alpha-2 codes are stored locally at:
File path: /afs/slac/package/pinger/intensity-map/country-codes.csv File format: #Country,code Pakistan, PK Korea Rep, KR United States,US
Demographics:
File path: /afs/slac/package/pinger/motion-chart/population.csv File format: #Country, Year, Population, Internet Users, Internet Penetration, DOI, HDI United States, 2008, 303824646, 213364918, 70.7, 0.62, 0.951
PingER data (obtained from prmout) Average RTT, Jitter (IPDV), Minimum RTT, Normalized Throughput, Packet Loss and Unreachability as seen from CERN, SLAC and ICTP:
File formats: allyearly: #allyearly,?,1998,1999,2000,2001,2002,2003,2004,2005,2006,2007,2008 monthly: #monthly,?,Oct2008,Sep2008,Aug2008, ... Jan2007,Dec2006,Nov2006 last60days: #last60days,?,08Oct27,08Oct26,08Oct25,08Oct24, ... 08Aug31,08Aug30,08Aug29 File paths: /afs/slac/g/www/www-iepm/pinger/prmout/average_rtt-CH.CERN.N2-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/average_rtt-CH.CERN.N2-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/average_rtt-CH.CERN.N2-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/average_rtt-EDU.SLAC.STANFORD.N3-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/average_rtt-EDU.SLAC.STANFORD.N3-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/average_rtt-EDU.SLAC.STANFORD.N3-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/average_rtt-IT.ICTP.N1-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/average_rtt-IT.ICTP.N1-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/average_rtt-IT.ICTP.N1-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/ipdv-CH.CERN.N2-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/ipdv-CH.CERN.N2-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/ipdv-CH.CERN.N2-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/ipdv-EDU.SLAC.STANFORD.N3-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/ipdv-EDU.SLAC.STANFORD.N3-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/ipdv-EDU.SLAC.STANFORD.N3-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/ipdv-IT.ICTP.N1-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/ipdv-IT.ICTP.N1-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/ipdv-IT.ICTP.N1-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/minimum_rtt-CH.CERN.N2-country-allyearly.cvs /afs/slac/g/www/www-iepm/pinger/prmout/minimum_rtt-CH.CERN.N2-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/minimum_rtt-CH.CERN.N2-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/minimum_rtt-EDU.SLAC.STANFORD.N3-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/minimum_rtt-EDU.SLAC.STANFORD.N3-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/minimum_rtt-EDU.SLAC.STANFORD.N3-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/minimum_rtt-IT.ICTP.N1-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/minimum_rtt-IT.ICTP.N1-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/minimum_rtt-IT.ICTP.N1-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/nthroughput-CH.CERN.N2-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/nthroughput-CH.CERN.N2-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/nthroughput-CH.CERN.N2-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/nthroughput-EDU.SLAC.STANFORD.N3-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/nthroughput-EDU.SLAC.STANFORD.N3-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/nthroughput-EDU.SLAC.STANFORD.N3-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/nthroughput-IT.ICTP.N1-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/nthroughput-IT.ICTP.N1-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/nthroughput-IT.ICTP.N1-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/packet_loss-CH.CERN.N2-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/packet_loss-CH.CERN.N2-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/packet_loss-CH.CERN.N2-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/packet_loss-EDU.SLAC.STANFORD.N3-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/packet_loss-EDU.SLAC.STANFORD.N3-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/packet_loss-EDU.SLAC.STANFORD.N3-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/packet_loss-IT.ICTP.N1-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/packet_loss-IT.ICTP.N1-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/packet_loss-IT.ICTP.N1-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/unreachability-CH.CERN.N2-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/unreachability-CH.CERN.N2-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/unreachability-CH.CERN.N2-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/unreachability-EDU.SLAC.STANFORD.N3-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/unreachability-EDU.SLAC.STANFORD.N3-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/unreachability-EDU.SLAC.STANFORD.N3-country-monthly.csv /afs/slac/g/www/www-iepm/pinger/prmout/unreachability-IT.ICTP.N1-country-allyearly.csv /afs/slac/g/www/www-iepm/pinger/prmout/unreachability-IT.ICTP.N1-country-last60days.csv /afs/slac/g/www/www-iepm/pinger/prmout/unreachability-IT.ICTP.N1-country-monthly.csv
Country to region mapping:
File path: /afs/slac.stanford.edu/www/comp/net/mon/countries.tsv File format: Country Name Region Name Note that the country name and region name are separated by a tab.
Implementation
Following is the pseudo code for implementation:
1. Load the country to region mapping to the hash-table "%region" which uses the country
names (as defined by PingER) as the index.
e.g. $region{'Kenya'} = Africa and
$region{'Korea, Rep'} = East Asia.
2. Load the PingER data to the data structure "%data" which uses PingER metrics as the index.
Here each entry stores a reference to a unique hash-table defined to store the PingER data
related to a country. Understandably the second hash-table uses country names as the index.
Each entry of the hash-table stores a reference to another hash-table used for storing
performance measurements for the country. Here timestamps are used as indices.
e.g. $data{'minrtt'}->{'Kenya'}->{'1998'},
$data{'packetloss'}->{'United States'}->{'Oct2008'} and
$data{'ipdv'}->{'Pakistan'}->{'08Oct27'}
The metrics used are: minrtt, avgrtt, nthroughput, ipdv, packetloss and unreachability.
An important factor to consider here is the file format. The yearly data from prmout is listed
in an ascending order from left to right, where as for monthly and daily data, the
measurements are listed in a descending order. Thus while loading, the iterators need to
be defined accordingly.
3. Load the demographics to the data structure "%data" which uses labels as the index.
Here each entry stores a reference to a unique hash-table defined to store the demographics
of a country. The second hash-table uses country names as the index. Each entry of
the hash-table stores a reference to another hash-table used for storing statistics
for the country. Here timestamps are used as indices.
e.g. $data{'inetpenetration'}->{'Pakistan'}->{'08Oct27'}
The metrics used are: inetusers and inetpenetration.
4. Generate the .html file in compliance with the Google Visualization API constraints.
Here the data is loaded in the following format (code in Javascript):
data.addColumn('string', '', 'Country');
data.addColumn('number', 'Average RTT (ms)', 'a');
data.addColumn('number', 'Normalized Throughput (Kbps)', 'b');
data.addColumn('number', 'Internet Users (#)', 'c');
data.addColumn('number', 'Internet Penetration (%)', 'd');
data.addColumn('number', 'Minimum RTT (ms)', 'e');
data.addColumn('number', 'Packet Loss (%)', 'f');
data.addColumn('number', 'Unreachability (%)', 'g');
data.addColumn('number', 'Jitter (ms)', 'h');
N.B. the data must be uploaded in a chronologically ascending order.
5. Create .html files for each vantage point: CERN Switzerland (CH.CERN.N2),
SLAC USA (EDU.SLAC.STANFORD.N3), ICTP Italy (IT.ICTP.N1)
for each region: Africa, Balkans, Central Asia, East Asia, Europe, Latin America,
Middle East, North America, Oceania, S.E. Asia, South Asia, World, for each
granularity: Yearly (since 1998), Monthly (last 20 months) and Daily (last 3 weeks).
Updating data files
The PingER data files are generated using the prm scripts. There are some trscronjobs running under pinger@pinger.slac.stanford.edu. They appear as:
################################################################################
#prm.pl runs monthly to create the executive plots to be accessesd via:
#http://www-iepm.slac.stanford.edu/pinger/prmout/
pinger;600 30 02 1 * * /afs/slac.stanford.edu/package/pinger/prm/prm.pl \
-c /afs/slac.stanford.edu/package/pinger/prm/prm-allmonthly.conf
#And yearly (Jan 5th at 4:30am) to get the yearly data.
pinger;600 30 04 05 1 * /afs/slac.stanford.edu/package/pinger/prm/prm.pl \
-c /afs/slac.stanford.edu/package/pinger/prm/prm-allyearly.conf
#It also runs each morning to update the last 21 days information.
pinger;600 30 00 * * * /afs/slac.stanford.edu/package/pinger/prm/prm.pl \
-c /afs/slac.stanford.edu/package/pinger/prm/prm-last60days.conf
#Create the motion bubble plots:
pinger;10 00 08 * * * /afs/slac/package/pinger/motion-chart/create-motion-chart-pages.pl
On the other hand, the demographics need to be updated manually. One of the simplest methods would be to load the flat file in Microsoft Excel and use the VLOOKUP method to add additional statistics in new columns for each country. Note that each country defines a new row of data for each year. Consider the following example:
#Country,Year,Population,Internet Users,Internet Penetration,DOI,HDI Pakistan,1998,139062987,7500000,4.7,0.26,0.551 Pakistan,1999,142520124,7500000,4.7,0.26,0.551 Pakistan,2000,146404914,7500000,4.7,0.26,0.551 Pakistan,2001,150399566,7500000,4.7,0.26,0.551 Pakistan,2002,153470779,7500000,4.7,0.26,0.551 Pakistan,2003,156196488,7500000,4.7,0.26,0.551 Pakistan,2004,159266367,7500000,4.7,0.26,0.551 Pakistan,2005,162490385,7500000,4.7,0.26,0.551 Pakistan,2006,165873928,7500000,4.7,0.26,0.551 Pakistan,2007,169340538,7500000,4.7,0.26,0.551 Pakistan,2008,172800051,7500000,4.7,0.26,0.551 ... United States,1998,276115288,213364918,70.7,0.62,0.951 United States,1999,279294713,213364918,70.7,0.62,0.951 United States,2000,282338631,213364918,70.7,0.62,0.951 United States,2001,285023886,213364918,70.7,0.62,0.951 United States,2002,287675526,213364918,70.7,0.62,0.951 United States,2003,290342554,213364918,70.7,0.62,0.951 United States,2004,293027571,213364918,70.7,0.62,0.951 United States,2005,295734134,213364918,70.7,0.62,0.951 United States,2006,298444215,213364918,70.7,0.62,0.951 United States,2007,301139947,213364918,70.7,0.62,0.951 United States,2008,303824646,213364918,70.7,0.62,0.951
Note that the country names used here must be the same as those used by PingER. The source of the data presented here is mentioned in the section 'Miscellaneous Details'.
Generating up-to-date Intensity Maps
To generate up to date .html files of the PingER intensity maps execute the following script:
/afs/slac/package/pinger/intensity-map/generate-intensity-maps.pl
The charts generated (listed below) may then viewed publicly via SLAC's web server.
/afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-1998.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-1999.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-2000.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-2001.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-2002.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-2003.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-2004.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-2005.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-2006.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-2007.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-CH.CERN.N2-2008.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-1998.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-1999.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-2000.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-2001.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-2002.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-2003.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-2004.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-2005.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-2006.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-2007.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-EDU.SLAC.STANFORD.N3-2008.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-IT.ICTP.N1-1998.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-IT.ICTP.N1-1999.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-IT.ICTP.N1-2000.html /afs/slac/g/www/www-iepm/pinger/intensity-maps/pinger-metrics-intensity-map-Africa-IT.ICTP.N1-2001.html 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Miscellaneous Details
- Tool: The interactive graph was generated using the Google Visualization API for intensity maps
- Data: The data presented here was collected by the PingER project, processed by prmout. The same data is available in tabular format. The statistics of Population were acquired from the U.S. Census Bureau, the country to region mapping was obtained by the geographical database maintained by the PingER project, where as the Internet usage statistics were taken from Internet World Stats. Please note that while all the statistics were acquired over several years (i.e. since 1998), the Internet usage statistics were documented in Nov. 2007.
- Loading: The time to load and render the web page is largely determined speed of the link, the file length (about a MByte that is determined by the number of metrics and frequency of data points and the number of metrics) and the speed of the client rendering.
- Metrics: Average RTT (ms), Normalized Throughput (Kbps), Internet Users (#), Internet Penetration (%), Minimum RTT (ms), Packet Loss (%), Unreachability (%), Jitter (ms),
- Authors: Umar Kalim & Les Cottrell 10/10/2008, version 1.3. Idea champion: Shahryar Khan.