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In the context of modern high-speed communication networks, decision reactivity is often complicated by the notion of guaranteed Quality of Service (QoS), which can either be related to time, packet loss or bandwidth requirements: constraints related to various types of QoS make some algorithms not acceptable. Due to emerging real-time and multimedia applications, efficient routing of information packets in dynamically changing communication network requires that as the load levels, | VNU Journal of Science Natural Sciences and Technology 24 2008 147-161 Learning approaches to support dynamics in communication networks Abdelhamid Mellouk1 Said Hoceini1 Saida Ziane1 Malika Bourennane2 1LISSI SCTIC Laboratory IUT Creteil Vitry University Paris XII France. 122 rue Paul Armangot 94400 Vitry sur Seine France Department of Computer Science University Es Senia Algeria Received 31 October 2007 Abstract In the context of modern high-speed communication networks decision reactivity is often complicated by the notion of guaranteed Quality of Service QoS which can either be related to time packet loss or bandwidth requirements constraints related to various types of QoS make some algorithms not acceptable. Due to emerging real-time and multimedia applications efficient routing of information packets in dynamically changing communication network requires that as the load levels traffic patterns and topology of the network change the decision policy also adapts. We focused in this paper on QoS based mechanisms by developing a neuro-dynamic programming to construct dynamic state-dependent policies. In this paper we present an accurate description of the current state- of-the-art and give an overview of our work in the use of reinforcement learning concepts focused on communications networks. We focus our attention by developing a system based on this paradigm and study the use of reinforcement learning approaches in three different communication networking domains wired networks mobile ad hoc networks and packet router s scheduling networks. Keywords Self-Depedent Mechanism Decision Quality of Service based Routing Multi Path Routing. Dynamic Networks Reinforcement Learning Adaptive Scheduling. 1. Introduction Today providing a good quality of service QoS in irregular traffic networks is an important challenge. Besides the impressive emergence and the important demand of the rising generation of real-time Multi-service such as Data Voice VoD Video-Conference .
