**7. References**

326 Telecommunications Networks – Current Status and Future Trends

**Throughput (25k)**

**packets per second**

**6. Conclusion** 

model.

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Fig. 21. Theoretical and experimental throughputs with 25Kcycle analysis load.

has been calculated according to the method explained in subsection 5.3.

the real one. It shows, therefore, that the analytical model fits the real system.

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Fig. 18, Fig. 19, Fig. 20 and Fig. 21 show the comparison between the theoretical model's throughput for different values of N and the real probe's throughput measured experimentally (marked as LAB in the graph). 64 byte-length packets have been used in the lab test and its corresponding service rates in the theoretical calculation. The service rates

In all the cases, the throughput grows until a maximum is reached (saturation point). We also observe in these graphs that, with increasing N, the theoretical throughput is close to

In this chapter we have presented an analytical model that represents a multiprocessor traffic monitoring system. This model analyses and quantifies the system performance and it can be useful to improve aspects related to hardware and software design. Even, the model can be extended to more complex cases which have not been treated in the laboratory.

Thus, the major contribution of this chapter is the development of a theoretical model based on a closed queuing network that allows to study the behaviour of a multiprocessor network probe. A series of simplifications and adaptations is proposed for the closed network, in order to fit it better to the real system. We obtain the model's analytic solution and we also propose a recurrent calculation method based on the mean value analysis. The model has been validated comparing theoretical results with experimental measures. In the validation process we have made use of a testing architecture that not only has measured the performance, it has also provided values for some necessary input parameters of the mathematical model. Moreover, the architecture helps to setup tests faster as well as to collect and plot results easier. Ksensor, a real probe, is part of the testing architecture and, therefore, it is directly involved in the validation process. As has been seen in the validation section, Ksensor's throughput is acceptably calculated by the model proposed in this chapter. The conclusions obtained have been satisfactory with regard to the behaviour of the

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LAB N=1 N=2 N=3 N=8 N=16 N=40


**14** 

**Routing and Traffic Engineering in** 

Mihael Mohorčič and Aleš Švigelj

*Jožef Stefan Institute* 

*Slovenia* 

**Dynamic Packet-Oriented Networks** 

Spurred by the vision of seamless connectivity anywhere and anytime, ubiquitous and pervasive communications are playing increasingly important role in our daily lives. New types of applications are also affecting behaviour of users and changing their habits, essentially reinforcing the need for being always connected. This clearly represents a challenge for the telecommunications community especially for operating scenarios characterised by high dynamics of the network requiring appropriate routing and traffic

Routing and traffic engineering are cornerstones of every future telecommunication system, thus, this chapter is concerned with an adaptive routing and traffic engineering in highly dynamic packet-oriented networks such as mobile ad hoc networks, mobile sensor networks or non-geostationary satellite communication systems with intersatellite links (ISL). The first two cases are recently particularly popular for smaller scale computer or data networks, where scarce energy resources represent the main optimisation parameter both for traffic engineering and routing. However, they require a significantly different approach, typically based on clustering, which exceeds the scope of this chapter. The third case, on the other hand, is particularly interesting from the aspect of routing and traffic engineering in large scale telecommunication networks. Even more so, since it exhibits a high degree of regularity, predictability and periodicity. It combines different segments of communication network and generally requires distinction between different types of traffic. Different restrictions and requirements in different segments typically require separate optimization

So, in order to explain all routing functions and different techniques used for traffic engineering in highly dynamic networks we use as an example the ISL network, characterized by highly dynamic conditions. Nonetheless, wherever possible the discussion is intentionally kept independent of the type of underlying network or particular communication protocols and mechanisms (e.g. IP, RIP, OSPF, MPLS, IntServ, DiffServ, etc.), although some presented techniques are an integral part of those protocols. Thus, this chapter is focusing on general routing and traffic engineering techniques that are suitable for the provision of QoS in packet-oriented ISL networks. Furthermore, most concepts,

**1. Introduction** 

engineering.

of resource management.

