Descriptive Network Analysis

The exploration commences by analysing the essential features of the traceroute data collected using our active Internet periphery measurements through the Portolan (2015) (see section 3.3.4). Figure 4-1 below provides a look and feel for our collected 731,200 individual traceroute hop observations.

Figure 4-1: Example of collected Paris traceroute observations.

As the different columns in Figure 4-1 above indicate, each traceroute hop (consist of a source IP address linking to a destination IP address) observation contains the following information:

• Traceroute identifier.

• The randomly-chosen destination of a given traceroute.

• Campaign identifier consisting of an identifier for the associated country of initial connection (‘WORLDin’ indicates India) and an identifier for the Autonomous System Number (e.g. ‘24560’) of the initial connection.

• Timestamp, comprised of YYYY-MM-DD and the exact record time. • Geo-location (Latitude, Longitude) of the data-collecting device. • The operating system of the data-collecting device (e.g. ‘android’).

• Associated hop number of a traceroute (e.g. third hop / step of a given

traceroute) to which the row refers (primary observation unit used in the

following Complex and Statistical Network Analysis in the rest of the dissertation).

• Target IP address (arrival point of the hop within a traceroute). • Round Trip Time (RTT) of a given hop.

• Binary indication whether or not a traceroute hop observation contains a skip (e.g. ‘1’ representing a failure when the connection between a source IP address and a destination IP address in the hop is not reachable or terminates, ‘0’ otherwise).

The complete dataset indicated the presence of traceroutes starting from both mobile broadband and Wi-Fi connections covering different locations. Here, only those measurements of the three Tamil Nadu mobile broadband operators were of interest. Hence, we separated the traceroute observations originating from Wi-Fi, from those

traceroutes originating from the mobile broadband operators that will be used to compare

our three operators of interest thoroughly. Given the nature of a traceroute, each collected observation incorporates a multitude of traceroute hops (or steps along a connection). Filtering the data by the identifier revealed that the total traceroute hop observations consisted of 57,121 unique traceroutes (each containing multiple hops), including those originating from Wi-Fi connections. The randomly-chosen destinations further exposed that the Portolan (2015) application randomly assigned 32,068 unique destinations for these 57,121 traceroute observations. Here, the random selection is used to replicate, in a possibly unbiased way, the behavioural patterns of end-users. Moreover, the campaign identifiers revealed that the recorded traceroute hop observations were commencing from twelve distinct Autonomous Systems Numbers (see Table 9-9 in the Appendices). By using the Hurricane Electric (2016) BGP-Toolkit, these campaign identifiers were associated with their organisational name. Revealing these names allowed us to choose only those non-Wi-Fi originating observations that are of fundamental interest for our analysis of the mobile operators’ upstream connectivity. Hence, this step was crucial in selecting and filtering the relevant dataset that will be used in the following analysis. We implemented this step by verifying the campaign identifiers using the Maxmind (2015) GeoIP2 database, together with UltraTools (2016), Team Cymru (2016) and the Hurricane Electric (2016) BGP Toolkit. Linking the Autonomous System Numbers to the collected traceroute hop dataset revealed that most of these collected hop observations belonged to traceroutes originating from the Wi-Fi based Spectranet (AS10029) and C48 Okhla Industrial Estate (AS55410), see Appendices.

After filtering out the set of observations originating from Wi-Fi connections we were left with only 36,388 total traceroute hop observations being relevant to our case study. Those represent the only connections originating from the Tamil Nadu mobile broadband operators. More specifically, Vodafone indicated 30,633 mobile broadband observations, followed by Aircel with 4,749 ones and Bharti Airtel with 956 observations. The filtered out 649,812 traceroute hop observations resulted, as the time stamps confirm, from Wi- Fi connections mainly captured during off and night-time hours by the Android smartphones, also containing observations from other locations.

Table 4-1 below indicates the total number of traceroutes (column 2 in Table 4-1) and the total number of traceroute hop observations (column 3 in Table 4-1) that were obtained for each of the three Tamil Nadu mobile broadband operators. Here, the average number of hops per traceroute observation is interesting since it indicates that Vodafone needed, on average, considerably more hops to complete a given traceroute than the other two operators.

Traceroute hop observations by operator

Mobile broadband operator Number of traceroute observations per mobile broadband operator Number of traceroute hop observations contained in all the mobile broadband operator-originated traceroutes Average Number of traceroute hop observations per traceroute observation Aircel 622 4,749 7.63 Bharti Airtel 148 956 6.46 Vodafone 2,678 30,633 11.44

Table 4-1: Traceroute hop observations by mobile broadband operator.

The associated number of hops describes the actual number of steps that a traceroute needed to take in order to reach its randomly-assigned final destination, through the routers, identified via their unique IP addresses, forming the basic steps of the observed internetworking through the Internet. Interestingly, we discovered that the path length of the Aircel traceroute observations ranged between 5-40 hops, the Bharti Airtel one ranged between 4-36, and the Vodafone one between 5-51 hops.

initial source IP address to the destination one, plus the time it takes for this to be acknowledged by the destination IP address and returned to the source IP address. An analysis of the RTT indicated, as the following Table 4-2 illustrates, that the lowest Round-Trip-Time of a completed traceroute was reached by Vodafone (0,042ms). These results are, in contradiction with the previous ones on the average hops per traceroute observation of each mobile broadband operator. This might indicate that there is a large amount of potential connections between IP addresses belonging to the same Autonomous System, as we will explain later in more detail. Furthermore, the Bharti Airtel observations revealed the largest range between the lowest and highest Round- Trip-Times, indicating that end-users might experience fluctuations in their perceived Quality of Service (QoS).

Round-Trip-Time (RTT) by mobile broadband operator Mobile broadband

operator RTT Low in ms RTT High in ms Range

Aircel 0,042384 1006,07 1006.028 Bharti Airtel 0,106708 2019,58 2019.473 Vodafone 0,044219 1243,9 1243.856 Key ms: milliseconds. RTT: Round-Trip-Time.

Table 4-2: Round-Trip-Time by mobile broadband operator.

Table 4-3 below displays the skip-distributions for each of the three operators, i.e. the frequency distribution of traceroutes, depending on the specific step (hop) along the

traceroute, where the connection fails (skips). Comparing the mobile broadband operator traceroute skip-distributions potentially revealed another indicator for perceived Quality

Skip distribution by mobile broadband operator Mobile broadband operator Skip 0 Skip 1 Skip 2 Skip 3 Skip 4 Skip 5 Skip 6 Skip 7 Skip 8 Skip 9 Vodafone 30,240 197 96 36 26 11 6 7 6 8 Bharti Airtel 884 58 13 1 0 0 0 0 0 0 Aircel 4,670 60 11 3 4 1 0 0 0 0

Table 4-3: Skip distribution by mobile broadband operator.

Summarising, the above Descriptive Network Analysis provides initial insights about some aspects of the internetworking features of the three mobile broadband operators, eventually affecting Quality of Service from an Internet periphery perspective. We showed that 36,388 of the 731,200 traceroute hop observations were relevant for this case study and compared the general properties of the observations per mobile broadband operator of interest. The following section aims to further uncover the distinct connectivity features in the upstream Internet access market of the three mobile broadband operator networks using Complex Network Analysis.