**3.1 Module for global distribution of traffic sources and destinations**

The module for global distribution of traffic sources and destinations should support the representation of an arbitrary distribution.

A simple representative of a geographically dependent source/ destination distribution assumes homogeneous distribution over the landmasses, considering continents and major islands (called landmass distribution), while traffic intensity above the oceans equals 0 (Mohorcic et al., 2002b). More realistic source/destination distributions should reflect the geographic distribution of traffic intensity, which is related to several techno-economic factors including the population density and distribution, the existing telecommunication infrastructure, industrial development, service penetration and acceptance level, gross domestic product (GDP) in a given region, and pricing of services and terminals (Werner & Maral, 1997, Hu & Sheriff, 1997, Werner & Lutz 1998). Thus, the estimation of traffic distribution in the yet non-existing system demands a good understanding of the types of services and applications that will be supported by the network. Furthermore, it should also consider attractiveness of particular services for potential users, which in turn depends also on different socio-economic factors.

The methodology for estimating the market distribution for different terminal classes, i.e. lap-top, briefcase and hand-held, is reported in (Hu & Sheriff, 1998) Essentially, countries over the globe are categorized into three different bands according to their annual GDP per capita: low (less than 6 kEuro), medium (between 6 kEuro and 22 kEuro) and high (greater than 22 kEuro). A yearly growth for GDP per capita for each country is then predicted by linearly extrapolating historical data. This, together with the tariff of a particular service and a predicted market saturation value, is used to determine the yearly service take-up for each country via the logistic model. The yearly service penetration for each country is estimated by multiplying the predicted yearly gross potential market with the yearly take-up (Mohorcic et al., 2003).

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 module for global distribution of traffic sources and destinations should support the

A simple representative of a geographically dependent source/ destination distribution assumes homogeneous distribution over the landmasses, considering continents and major islands (called landmass distribution), while traffic intensity above the oceans equals 0 (Mohorcic et al., 2002b). More realistic source/destination distributions should reflect the geographic distribution of traffic intensity, which is related to several techno-economic factors including the population density and distribution, the existing telecommunication infrastructure, industrial development, service penetration and acceptance level, gross domestic product (GDP) in a given region, and pricing of services and terminals (Werner & Maral, 1997, Hu & Sheriff, 1997, Werner & Lutz 1998). Thus, the estimation of traffic distribution in the yet non-existing system demands a good understanding of the types of services and applications that will be supported by the network. Furthermore, it should also consider attractiveness of particular services for potential users, which in turn depends also

The methodology for estimating the market distribution for different terminal classes, i.e. lap-top, briefcase and hand-held, is reported in (Hu & Sheriff, 1998) Essentially, countries over the globe are categorized into three different bands according to their annual GDP per capita: low (less than 6 kEuro), medium (between 6 kEuro and 22 kEuro) and high (greater than 22 kEuro). A yearly growth for GDP per capita for each country is then predicted by linearly extrapolating historical data. This, together with the tariff of a particular service and a predicted market saturation value, is used to determine the yearly service take-up for each country via the logistic model. The yearly service penetration for each country is estimated by multiplying the predicted yearly gross potential market with the yearly take-up

The model is highly parameterized and consists of four main modules: • module for global distribution of traffic sources and destinations; • module for temporal variations of traffic sources' intensity; • module describing the traffic flow patterns between regions; and • module describing statistical behaviour of aggregated traffic sources.

**3.1 Module for global distribution of traffic sources and destinations** 

representation of an arbitrary distribution.

on different socio-economic factors.

(Mohorcic et al., 2003).

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

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

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

Fig. 2. Daily user profile curve.

Routing and Traffic Engineering in Dynamic Packet-Oriented Networks 345

1997), but preferably on a smaller scale between countries/territories. In a destination region, the traffic can be divided among the satellites proportionally to their coverage of that region. Customized traffic flow patterns should be based on the density distribution of sources

Europe

Asia

Oceania

Africa

The fourth module concerns modelling of the aggregated traffic sources. In particular, the module comprises of suitable aggregate traffic source generator, which is modulated by the normalized cumulative traffic on each satellite obtained from distribution of traffic sources and destinations and temporal variation of traffic sources' intensity. Thus data packets are actually generated considering the relative traffic intensity experienced by a particular satellite in its coverage area, while taking into account the statistics of the selected aggregate

Ideally, the traffic source model should capture the essential characteristics of traffic that have significant impact on network performance with only a small number of parameters, and should allow fast generation of packets. Among the most important traffic characteristics for circuit switched networks are the connection duration distribution and the average number of connection requests per time unit. By contrast, in the case of packet switched networks, traffic characteristics are given typically by packet lengths and packet inter-arrival times (in the form of distributions or histograms), burstiness, moments, autocorrelations, and scaling (including long-range dependence, self-similarity, and multifractals). For generating cumulative traffic load on a particular satellite, the traffic source generator should model an aggregate traffic of many sources overlaid with the effect of a multiple access scheme, which is expected to significantly shape source traffic originating from single or multiplexed ground terminal applications due to the uplink

One approach for modelling aggregate traffic sources is by using traces of real traffic. Tracedriven traffic generators are recommended for model validation, but suffer from two

and/or destinations for the selected type of service.

North America

South America

resource management and traffic scheduling.

traffic source model.

Fig. 4. Geographical division of six source/destination regions.

**3.4 Module describing statistical behaviour of aggregated traffic sources** 

An alternative approach defines temporal variation of traffic load in conjunction with the global distribution of traffic sources and destinations, which inherently takes into account geographical time zones. An example of relative traffic intensity considering distribution of traffic sources and destinations combined with temporal variation of traffic load is depicted in Fig. 3, where traffic intensity is normalised to the highest value (i.e. the maximum value of normalized traffic load equals 1, but for better visualization we bounded the z-axis in Fig. 3 to 0.3). The traffic intensity is generated by assuming that a single session is established per day per user and that each session on average lasts for about 2 minutes.

Fig. 3. Global distribution and activity of traffic sources and destinations at midnight GMT.

Another contribution to temporal variation of traffic load in non-geostationary ISL networks in addition to user activity dynamics is the rapidly changing satellite visibility, and consequently active users' coverage, on the ground. To a certain extent this temporal variation as well as multiple visibility of satellites can be captured with a serving satellite selection scheme. Implementing a satellite selection scheme in case of multiple visibility has two aspects. For fixed earth stations line-of-sight conditions are assumed, so that the serving satellite can be determined according to a simple deterministic rule, e.g., maximum elevation satellite. For mobile earth stations, the stochastic feature of unexpected handover situations due to propagation impairments can be considered through the shares of traffic on alternative satellites also estimated according to a simple rule (e.g., equal sharing between all satellites above the minimum elevation) or using a simple formula (e.g., shares are a function of the elevation angle of each alternative satellite as one main indicator for channel availability).

#### **3.3 Module describing the traffic flow patterns between regions**

This module assigns traffic flow destinations using a traffic flow pattern resembling the flow characteristic between different regions. Interregional patterns should be defined at least on the level of the Earth's six continental regions shown in Fig. 4, similarly as in (Werner & Maral,

An alternative approach defines temporal variation of traffic load in conjunction with the global distribution of traffic sources and destinations, which inherently takes into account geographical time zones. An example of relative traffic intensity considering distribution of traffic sources and destinations combined with temporal variation of traffic load is depicted in Fig. 3, where traffic intensity is normalised to the highest value (i.e. the maximum value of normalized traffic load equals 1, but for better visualization we bounded the z-axis in Fig. 3 to 0.3). The traffic intensity is generated by assuming that a single session is established per day per user and that each session on average lasts for about 2 minutes.

Fig. 3. Global distribution and activity of traffic sources and destinations at midnight GMT.

Another contribution to temporal variation of traffic load in non-geostationary ISL networks in addition to user activity dynamics is the rapidly changing satellite visibility, and consequently active users' coverage, on the ground. To a certain extent this temporal variation as well as multiple visibility of satellites can be captured with a serving satellite selection scheme. Implementing a satellite selection scheme in case of multiple visibility has two aspects. For fixed earth stations line-of-sight conditions are assumed, so that the serving satellite can be determined according to a simple deterministic rule, e.g., maximum elevation satellite. For mobile earth stations, the stochastic feature of unexpected handover situations due to propagation impairments can be considered through the shares of traffic on alternative satellites also estimated according to a simple rule (e.g., equal sharing between all satellites above the minimum elevation) or using a simple formula (e.g., shares are a function of the elevation angle of each alternative satellite as one main indicator for channel availability).

This module assigns traffic flow destinations using a traffic flow pattern resembling the flow characteristic between different regions. Interregional patterns should be defined at least on the level of the Earth's six continental regions shown in Fig. 4, similarly as in (Werner & Maral,

**3.3 Module describing the traffic flow patterns between regions** 

1997), but preferably on a smaller scale between countries/territories. In a destination region, the traffic can be divided among the satellites proportionally to their coverage of that region.

Customized traffic flow patterns should be based on the density distribution of sources and/or destinations for the selected type of service.

Fig. 4. Geographical division of six source/destination regions.
