This paper deals with the performance evaluation of different channel resource management techniques in LEO satellite systems based on an earth-fixed cell concept. Furthermore, in order to reduce the handover failure probability, we assumed that handover attempts can be queued. Both fixed and mobile users have been considered resulting in several classes of users. Each class requires a given Quality of Service (QoS) and thus a fixed part of the shared resource. Two channel allocation techniques are investigated: fixed channel allocation (FCA) and dynamic channel allocation (DCA). An analytical model is derived to analyze the performance of the FCA scheme supporting different kinds of users. A second analytical approch is proposed, in the FCA case, where a handover queuing strategy is taken into account. Implementation aspects for FCA and DCA strategies are discussed and compared in terms of blocking probabilities relative to each type of users.

Intelligent Network (IN) is a distributed architecture allowing telecom companies to create and to customize services. Network and services have to be integrated in the management process provided by the IN. The integration of the Intelligent Network and the TMN (Telecommunications Management Networks) can be useful to achieve the management process. Our proposal is an evolution towards a more intelligent management structure provided through the Distributed Artificial Intelligence concepts.

The new Internet has to provide the Quality of Services to converged multimedia services, in which each one may choose its own requirements. Managing such a dynamic network is not an easy task. A more intelligent and adaptive behavior is required from network management. We argue that agents are able to realize this task by dynamically adapting management mechanisms to the current network conditions. This article presents a Behavioral Multi-Agent-based model for QoS-enabled Internet. Based on this behavioral approach, we analyze network management mechanisms (or "elementary behaviors") in terms of performance and applicability profile. We use simulation to observe services performances when submitted to diverse QoS management elementary behaviors.