**3. Traffic modelling for global networks**

As we have shown in previous section, the general routing and traffic engineering functions consist of many different algorithms, methods and policies that need to be carefully selected and adapted to the particular network characteristics as well as types of traffic to be used in the network. Clearly, the more dynamic and non-regular the network and the more different types of traffic, the more demanding is the task of optimising network performance, requiring good understanding of the fundamental network operating conditions and the traffic characteristics. The later largely affect the performance of routing and traffic engineering, typically requiring appropriate traffic models to be used in simulating, testing and benchmarking different routing and traffic engineering solutions. In the following a methodology is described for developing a global traffic model suitable for supporting the dimensioning and computer simulations of various procedures in the global networks but focusing in particular on the non-geostationary ISL networks, which are well suited for supporting asymmetric applications such as data, audio and video streaming, bulk data transfer, and multimedia applications with limited interactivity, as well as the broadband access to Internet services beyond densely populated areas. Such traffic models are an important input to network dimensioning tasks (Werner et al., 2001) as well as to simulators devoted to the performance evaluation of particular network functions such as routing and traffic engineering (Mohorcic et al., 2001, 20021, Svigelj et al., 2004a).

A typical multimedia application contains a mix of packets from various sources. Purely mathematical traffic generators cannot capture the traffic characteristics of such applications in real networks to the extent that would allow detailed performance evaluation of the

Routing and Traffic Engineering in Dynamic Packet-Oriented Networks 343

Taking into account techno-economic and socio-economic factors and the above methodology, we can define different non-homogeneous geographic-dependent distributions taking into account a more realistic distribution of sources and destinations for provisioning of the particular types of service. Such geographic-dependent distributions are typically based on statistical data provided on the level of countries, and only for some larger countries also on the level of states and territories. In addition to limitations of data availability, we also face the problem of the accuracy of its representation, which depends on the granularity of the model and on the assumption regarding the source/destination distribution within the smallest geographical unit (i.e. country). The simplest approach in country-based non-homogeneous geographic-dependent distributions assumes that a nation's subscribers are evenly distributed over the country. The weakness of this approach is representation of traffic demand in large countries spanning several units of geographical granularity. In determining the distribution, different levels of geographical granularity may be adopted; however, in order to be able to individually represent also small countries, the geographical granularity should be in the range of those small countries. In (Mohorcic et al., 2003), a traffic grid of dimension 180° × 360°

Temporal variation of traffic load in a non-geostationary satellite system is caused by daily variation of traffic load due to the local time of day and geographical variation of this daily load behaviour according to geographical time zones. Both are considered in the module for temporal variation of traffic load, which actually mimics the geographically dependent daily behaviour of users. Daily variation can be taken into account with an appropriate daily user profile curve (for average or for local users). An example of such a daily user profile curve is shown in Fig. 2. For geographical time zones a simplified model can be considered, which

has been generated in steps of 1° in both latitude and longitude directions.

increments the local hour every 15 degrees longitude eastward from the GMT.

**3.2 Module for temporal variations of traffic sources' intensity** 

Fig. 2. Daily user profile curve.

network. Hence, the applicability of traffic analysis based on mathematical tractability is diminishing, while the importance of computer simulation has grown considerably, but poses different requirements for traffic source models (Ryu, 1999). A suitable traffic source model should represent real traffic, while the possibility of mathematical description is less important. In global non-geostationary satellite network traffic source model needs to be complemented by a suitable model of other elementary phenomena causing traffic dynamics, i.e. geographical distribution of traffic sources and destinations, temporal variation of traffic load and traffic flow patterns between different geographical regions.

In the following the approach to modelling global aggregate traffic intensity is described, in particular useful for the dimensioning of satellite networks and computer simulations of various procedures in the ISL network segment, including routing and traffic engineering.

The model is highly parameterized and consists of four main modules:

