Une approche modulaire probabiliste pour le routage à Qualité de Service intégrée

英文摘要：

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 routing policy also adapts. We focused in this paper on QoS based routing by developing a neuro-dynamic programming to construct dynamic state dependent routing policies. We propose an approach based on adaptive algorithm for packet routing using reinforcement learning which optimizes two criteria: cumulative cost path and end-to-end delay. Numerical results obtained with OPNET simulator for different packet interarrival times statistical distributions with different levels of traffic's load show that the proposed approach gives better results compared to standard optimal path routing algorithms.