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Adaptive Probabilistic Routing Schemes for Real Time Traffic in High Speed Dynamic Networks


Abdelhamid Mellouk, Samia Larynouna, Sa?d Hoce?ni


Vol. 6  No. 5  pp. 36-42


Routing is a relevant issue for maintaining good performance and successfully operating in a network. We focused in this paper on neuro-dynamic programming to construct dynamic state-dependent routing policies which offer several advantages, including a stochastic modelization of the environment, learning and evaluation are assumed to happen continually, multi-paths routing and minimizing state overhead. In this paper, we propose an approach based on adaptive algorithm for packet routing using reinforcement learning called K Shortest Paths Q Routing which gives much more better performances compared to standard shortest path, K-Shortest algorithm and Q-routing algorithms. To improve the distribution of the traffic on several paths, we integrate a probabilistic module in order to compute dynamically a probabilistic traffic distribution. This module takes into account the capacity of the queuing file in the router and the average packet delivery time. The performance of our algorithm is evaluated experimentally with OPNET simulator for different levels of traffic’s load and compared to standard shortest path and Q-routing algorithms on large interconnected network. Our approach prove superior to a classical algorithms and is able to route efficiently in large networks even when critical aspects, such as the link broken network, are allowed to vary dynamically.


Quality of Service based Routing, Multi Path Routing, Dynamic Network, High Irregular Traffi