Une approche modulaire probabiliste pour le routage \`a Qualit\'e de Service int\'egr\'ee

TL;DR

This paper presents a modular probabilistic approach using reinforcement learning for adaptive QoS routing in dynamic networks, improving performance over standard algorithms.

Contribution

It introduces a neuro-dynamic programming method for real-time, state-dependent routing that adapts to network changes and optimizes multiple QoS criteria.

Findings

Better performance than standard routing algorithms

Effective adaptation to changing network conditions

Improved end-to-end delay and path cost

Abstract

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.