Demystifying Google Global Cache

The amount of data Google serves through the Internet is undoubtedly enormous and their network is correspondingly efficient in doing so. One key component of Google’s information superhighway is their CDN-Like Cache infrastructure: the Google Global Cache. We ran some tests with ProbeAPI and had a closer look at this vast network, which enables us to enjoy Youtube at the best quality our ISP’s capacities allow.

Our experiment showed us the extent of the deployed network in its worldwide coverage. We were able to observe the impressive number of 2383 Cache Instances across 800 locations all around the globe. It is important to take into account, that there may be more locations where a particular ISP wasn’t available through any probe when we ran the experiment or, for example, we have no probes in an ISP which runs through a particular Cache Server, especially in remote locations where the presence of ProbeAPI Probes is still scarce.

We used all the available probes at the moment we ran the experiment, delivering a total of 240184 individual results, which were grouped and cross-linked to obtain relevant data.

The cache locations are codenamed with three letter airport-codes. Cross-linking the Airport codes from the IATA with the codes obtained from the Cache-Servers’ names, we were able to obtain their approximate geographical location.


Detected Cache Instances





North America


South America






Central America



The top 20 Countries with the highest number of detected Cache Locations are:



Detected Cache Instances

North America United States 296
Asia Russia 263
South America Brazil 220
Asia India 83
North America Canada 76
North America Mexico 70
Europe United Kingdom 67
Asia Japan 63
Europe Ukraine 59
Asia Thailand 51
Oceania Australia 48
Europe Poland 45
Asia Indonesia 38
Europe Germany 36
South America Argentina 31
Oceania New Zealand 27
Europe Spain 27
Europe Italy 27
Europe France 26
Asia Bangladesh 24


Top 25 Cities with the most Cache Locations detected:

City Country Detected Cache Instances Detected Networks Ratio Networks/Caches
Moscow Russia 42 918 21,9
Sao Paulo Brazil 31 740 23,9
Tokyo Japan 31 116 3,7
Rio De Janeiro Brazil 28 322 11,5
Kiev Ukraine 28 277 9,9
London United Kingdom 24 347 14,5
Dhaka Bangladesh 22 51 2,3
Bangkok Thailand 21 28 1,3
St. Petersburg Russia 18 102 5,7
Sofia Bulgaria 17 104 6,1
Yekaterinburg Russia 17 84 4,9
Buenos Aires Argentina 17 79 4,6
Jakarta Indonesia 17 28 1,6
Bucharest Romania 15 92 6,1
Belgrade Serbia 15 42 2,8
Budapest Hungary 14 123 8,8
Sydney Australia 14 54 3,9
Mumbai India 14 46 3,3
Montreal Canada 14 43 3,1
Auckland New Zealand 14 39 2,8
Warsaw Poland 13 318 24,5
New York United States 13 276 21,2
Novosibirsk Russia 13 76 5,8
Toronto Canada 13 71 5,5
Kuala Lumpur Malaysia 13 28 2,2

To give ourselves an idea of the number of users covered by our detected servers, we have ranked the 25 top countries in terms of the estimated number of users.

Country Detected Cache Instances est. Number of Users Users/Cache
India 83 236.000.000 2.845.000
United States 296 219.000.000 741.000
Brazil 220 105.000.000 478.000
Japan 63 93.000.000 1.478.000
Russian Federation 263 78.000.000 298.000
Indonesia 38 66.000.000 1.733.000
Germany 36 66.000.000 1.826.000
Nigeria 8 61.000.000 7.666.000
Mexico 70 61.000.000 870.000
France 26 52.000.000 2.000.000
United Kingdom 67 51.000.000 764.000
Egypt 13 45.000.000 3.455.000
Philippines 11 41.000.000 3.715.000
Vietnam 16 40.000.000 2.496.000
Turkey 1 35.000.000 35.212.000
Spain 27 34.000.000 1.271.000
Italy 27 34.000.000 1.268.000
Bangladesh 24 32.000.000 1.321.000
Colombia 22 30.000.000 1.375.000
Argentina 31 30.000.000 976.000
Pakistan 3 27.000.000 9.147.000
South Africa 14 23.000.000 1.642.000
Poland 45 22.000.000 500.000
Kenya 6 21.000.000 3.517.000

* Thanks to APNIC for helping us estimate the number of users in each network.

The following map pinpoints all the instances of Google Global Cache we could observe. Please remember that the pins are pointing to the cities’ airports and not their precise location within the city, which is close enough for describing their location for these purposes.

From the huge number of Probes we applied for the experiment, we got an impressive array of the variety of networks connected to each Cache Location.

When clicking on the pins, we can see a list of cache instances and below the corresponding list of connected networks to each cache.

Top 25 Cities with the most networks detected:

City Country Cache Instances Detected Networks Ratio Networks/Caches
Moscow Russia 42 918 21,9
Sao Paulo Brazil 31 740 23,9
Chicago United States 11 511 46,5
Frankfurt Germany 7 475 67,9
Washington United States 10 470 47,0
Paris France 12 405 33,8
Amsterdam Netherlands 8 383 47,9
Dallas-Fort Worth United States 9 356 39,6
London United Kingdom 24 347 14,5
Rio De Janeiro Brazil 28 322 11,5
Warsaw Poland 13 318 24,5
Prague Czech Republic 10 288 28,8
Kiev Ukraine 28 277 9,9
New York United States 13 276 21,2
Los Angeles United States 11 262 23,8
Miami United States 6 236 39,3
Atlanta United States 7 177 25,3
San Jose United States 3 159 53,0
Milan Italy 4 158 39,5
Katowice Poland 4 145 36,3
Belo Horizonte Brazil 12 131 10,9
Madrid Spain 9 125 13,9
Budapest Hungary 14 123 8,8
Tokyo Japan 31 116 3,7
Mountain View United States 2 109 54,5

We can observe, that there are locations with a high number of access points and also a high number of networks, but like in the case of Moscow or São Paulo, the relationship between the number of networks vs. number of access points is very high. A notable case is Tokyo, where 116 ISPs for 31 Cache Access Points were observed, giving a ratio of only 3,7.

It has to be taken into account that many access points are dedicated to different segments, for example, if we look at Berlin, Germany we will see that Vodafone and Telefonica Germany have their own dedicated access points (vodafone-ber1 and hansenet-ber2). On the other side, ber01s12 is probably owned by Deutsche Telekom AG, the biggest Telecom in Germany, which is giving access to other companies through their infrastructure, so we can see Verizon, Kabel Deutschland and also some other Telefonica users go through this other Access Point. Finally, if we observe ecix-ber1 we can see that that point seems to be reserved for Private Business ISPs, like e.discom, WEMACOM, the multimedia specialist MyWire or the private IT Infrastructure provider Macnetix.

Another important fact that has influence on the number of detected networks is our own coverage with ProbeAPI. In cases like Moscow, ProbeAPI has a high number of active probes in Russia, with Moscow having the highest concentration of them. That’s why this data has to be considered a snapshot of Google’s CDN taken with all our available probes at one particular time.

Following the same line, an interesting case is Turkey, where we could detect only one Cache Location, despite of having typically a high concentration of active Probes there. Turkey has been blocking access to Youtube and other Google services intermittently during the last years following political controversies, which could explain the low number of results obtained from a well covered region.


There is much mystery surrounding Google Global Cache which is one of Google’s most important pieces of infrastructure. We were able to gather an impressive number of information with one single measurement of ProbeAPI, which helped us to understand the extent and distribution of Google’s Cache Locations.

Taking into account that this information was made snapshot-like with one sole measurement, we gathered 2383 Cache Instances across 800 locations worldwide (at least) that Google is actively using throughout their partner network operators.

There are still some issues to be covered by more measurements over time which will surely reveal more networks and cache locations. This is one case where continuous monitoring will offer a good opportunity to get thorough measurements, not only in terms of traffic variability, but also in terms of coverage, as probes connect to ProbeAPI through different locations during any period of time.

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