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

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 traffic source model.

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 resource management and traffic scheduling.

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

Routing and Traffic Engineering in Dynamic Packet-Oriented Networks 347

For the more accurate prediction of the behaviour of the traffic source exhibiting long-range dependence, the traffic model requires detailed modelling of also the second order statistics of the packet arrival process. The accurate fitting of modelled traffic to the traffic trace can be achieved using modelling process with a discrete-time batch Markovian arrival process that jointly characterizes the packet arrival process and the packet length distribution (Salvador et al., 2004). Such modelling allows very close fitting of the auto-covariance, the

<sup>0</sup> <sup>200</sup> <sup>400</sup> <sup>600</sup> <sup>800</sup> <sup>1000</sup> <sup>1200</sup> <sup>1400</sup> <sup>1600</sup> <sup>1800</sup> <sup>2000</sup> <sup>0</sup>

packet length [bytes]

The potential drawback of traffic sources based on real traffic traces is that the empirically obtained traffic properties (i.e. obtained from the aggregated traffic on the backbone Internet link in this particular example) may not be suitably representative for the system under consideration, so it can sometimes deviate considerably from real situations and lead to

In addition to traffic sources based on traffic traces (directly or via statistical distributions) traffic sources can also be implemented in classical way with pure mathematical distributions such as Poisson, Uniform, Self-Similar, etc. Although such mathematically tractable traffic sources never fully resemble the characteristics of real traffic, they can serve as a reference point to compare simulation results obtained with different scenarios, however they should exhibit the same values of first order statistic (i.e. mean inter-arrival

In the case of supporting different levels of services, packets belonging to different types of traffic (e.g. real time, high throughput, best effort) should be generated using different traffic source models, which should reproduce statistical properties of that particular traffic. However, as different services and applications will generate different traffic intensity depending on regions and users' habits, also separate traffic flow patterns will have to be developed for different types of traffic, to be used in conjunction with different traffic source

Fig. 6. Packet length distribution obtained with empirical traffic generator.

time and average packet length) as obtained from traces.

marginal distribution and the queuing behaviour of measured traces.

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incorrect conclusions.

generators.

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drawbacks: firstly, the traffic generator can only reproduce something that has happened in the past, and secondly, there is seldom enough data to generate all possible scenarios, since the extreme situations are particularly hard to capture. In the case of satellite networks with no appropriate system to obtain the traffic traces, the use of traces is even more inconvenient.

An alternative approach, increasingly popular in the field of research, is to base the modelling of traffic sources on empirical distributions obtained by measurement from real traffic traces. The measurements can be performed on different segments of real networks, i.e. in the backbone network or in the access segment. In order to generate cumulative traffic load representing an aggregate of many individual traffic sources in the coverage area of the satellite, the traffic properties have to be extracted from a representative aggregate traffic trace (Svigelj at al., 2004a), such as a real traffic trace captured on the 622 Mbit/s backbone Internet link carrying 80 Mbit/s traffic (Micheel, 2002). The selected traffic trace comprises aggregate traffic from a large number of individual sources. Such traffic trace resembles the traffic load experienced by a satellite, both from numerous traffic sources within its coverage area, and from aggregate flows transferred over broadband intersatellite links. A suitable traffic source model, which resembles IP traffic in the backbone network, can already be built by reproducing some of the first order statistical properties of the real traffic trace that have major impact on network performance, e.g. inter-arrival time and packet length distribution. A simple traffic generator can be developed using a look-up table with normalized values, which allows packet inter-arrival time and packet length values to be scaled, so as to achieve the desired total traffic load. Distributions of packet inter-arrival time and packet length obtained with such a traffic generator are depicted in Fig. 5 and Fig. 6 respectively. The main advantage of traffic sources, whose distributions conform to those obtained by measurements of real traffic, is that they are relatively simple to implement and allow high flexibility.

Fig. 5. Packet inter-arrival time distribution obtained with empirical traffic generator.

drawbacks: firstly, the traffic generator can only reproduce something that has happened in the past, and secondly, there is seldom enough data to generate all possible scenarios, since the extreme situations are particularly hard to capture. In the case of satellite networks with no appropriate system to obtain the traffic traces, the use of traces is even more

An alternative approach, increasingly popular in the field of research, is to base the modelling of traffic sources on empirical distributions obtained by measurement from real traffic traces. The measurements can be performed on different segments of real networks, i.e. in the backbone network or in the access segment. In order to generate cumulative traffic load representing an aggregate of many individual traffic sources in the coverage area of the satellite, the traffic properties have to be extracted from a representative aggregate traffic trace (Svigelj at al., 2004a), such as a real traffic trace captured on the 622 Mbit/s backbone Internet link carrying 80 Mbit/s traffic (Micheel, 2002). The selected traffic trace comprises aggregate traffic from a large number of individual sources. Such traffic trace resembles the traffic load experienced by a satellite, both from numerous traffic sources within its coverage area, and from aggregate flows transferred over broadband intersatellite links. A suitable traffic source model, which resembles IP traffic in the backbone network, can already be built by reproducing some of the first order statistical properties of the real traffic trace that have major impact on network performance, e.g. inter-arrival time and packet length distribution. A simple traffic generator can be developed using a look-up table with normalized values, which allows packet inter-arrival time and packet length values to be scaled, so as to achieve the desired total traffic load. Distributions of packet inter-arrival time and packet length obtained with such a traffic generator are depicted in Fig. 5 and Fig. 6 respectively. The main advantage of traffic sources, whose distributions conform to those obtained by measurements of real traffic, is that they are relatively simple to implement and

<sup>0</sup> 0.05 0.1 0.15 0.2 0.25 0.3 0.35 0.4 <sup>0</sup>

inter arrival time [ms]

Fig. 5. Packet inter-arrival time distribution obtained with empirical traffic generator.

inconvenient.

allow high flexibility.

PDF [%]

For the more accurate prediction of the behaviour of the traffic source exhibiting long-range dependence, the traffic model requires detailed modelling of also the second order statistics of the packet arrival process. The accurate fitting of modelled traffic to the traffic trace can be achieved using modelling process with a discrete-time batch Markovian arrival process that jointly characterizes the packet arrival process and the packet length distribution (Salvador et al., 2004). Such modelling allows very close fitting of the auto-covariance, the marginal distribution and the queuing behaviour of measured traces.

Fig. 6. Packet length distribution obtained with empirical traffic generator.

The potential drawback of traffic sources based on real traffic traces is that the empirically obtained traffic properties (i.e. obtained from the aggregated traffic on the backbone Internet link in this particular example) may not be suitably representative for the system under consideration, so it can sometimes deviate considerably from real situations and lead to incorrect conclusions.

In addition to traffic sources based on traffic traces (directly or via statistical distributions) traffic sources can also be implemented in classical way with pure mathematical distributions such as Poisson, Uniform, Self-Similar, etc. Although such mathematically tractable traffic sources never fully resemble the characteristics of real traffic, they can serve as a reference point to compare simulation results obtained with different scenarios, however they should exhibit the same values of first order statistic (i.e. mean inter-arrival time and average packet length) as obtained from traces.

In the case of supporting different levels of services, packets belonging to different types of traffic (e.g. real time, high throughput, best effort) should be generated using different traffic source models, which should reproduce statistical properties of that particular traffic. However, as different services and applications will generate different traffic intensity depending on regions and users' habits, also separate traffic flow patterns will have to be developed for different types of traffic, to be used in conjunction with different traffic source generators.

Routing and Traffic Engineering in Dynamic Packet-Oriented Networks 349

Traffic engineering involves adapting the routing of traffic to the network conditions with two main goals: (i) providing sufficient quality of service, which is important from user's point of view, and (ii) efficient use of network resources, which is important for operators of telecommunication's network. The presented routing and traffic engineering issues addressed both goals that are explained using the ISL network as a concrete example of highly dynamic telecommunication network with several useful properties, which can be exploited by developing of routing procedures. However, the presented work is not limited to ISL networks, but can be used also in other networks as described in (Liu et al., 2011; Long et al., 2010; Rao & Wang, 2010, 2011). Routing and traffic engineering functions are

Adaptation of routing requires, in addition to good understanding of the fundamental network operating conditions, also good knowledge of the characteristics of different types of traffic in the network. In order to support better modelling of traffic characteristics a modular methodology is described for developing a global aggregate traffic intensity model suitable for supporting the dimensioning and computer simulations of various procedures in the global networks. It is based on the integration of modules describing traffic characteristics on four different levels of modelling, i.e. geographical distribution of traffic sources and destinations, temporal variations of traffic sources' intensity, traffic flows

Bertsekas D. & Gallager R. (1987). *Data Networks*, Englewood Cliffs: Prentice-Hall

Franck L. & Maral G. (2002a). Signaling for inter satellite link routing in broadband non

Franck L. & Maral G. (2002b). Routing in Networks of Intersatellite Links. *IEEE Transaction* 

Hu Y. F. & Sheriff R. E. (1997). The Potential Demand for the Satellite Component of the

Hu Y. F. & Sheriff R. E. (1999). Evaluation of the European Market for Satellite-UMTS Terminals. *International Journal of Satellite Communications*, Vol. 17, pp. 305-323. Liu, X.; Ma, J. & Hao, X. (2011). Self-Adapting Routing for Two-Layered Satellite Networks.

Long F; Xiong N.; Vasilakos A.V.; Yang L.T. & Sun, F. (2010). A sustainable heuristic QoS

Micheel, 2002. National Laboratory for Applied Network Research), Passive Measurement

Mohorcic M.; Svigelj A. & Kandus G. 2004. Traffic Class Dependent Routing in ISL Networks. *IEEE Transaction on Aerospace and Electronic Systems,* Vol. 39, pp. 1160-1172. Mohorcic M.; Svigelj A.; Kandus G. & Werner M. (2000). Comparison of Adaptive Routing

Universal Mobile Telecommunication System. *Electronics and Communication* 

routing algorithm for pervasive multi-layered satellite wireless networks. *Wireless* 

Algorithms in ISL Networks Considering Various Traffic Scenarios. In: *Proc. of 4th* 

GEO satellite systems. *Computer Networks*, Vol. 39, No. 1, pp. 79-92.

*on Aerospace and Electronic Systems,* Vol. 38, No. 3, pp. 902-917.

*China Communications*, Volume 8, Issue 4, July 2011, pp. 116-124.

*Networks*, Volume 16, Issue 6, August 2010, Pages 1657-1673.

and Analysis. http://pma.nlanr.net/PMA/, 22 October, 2002.

presented in modular manner for easier reuse of particular procedures.

patterns and statistical behaviour of aggregated traffic sources.

*Engineering Journal*, April 1997, pp. 59-67.

**4. Summary** 

**5. References** 

International.
