Improve End to End Delay with Q-Routing Algorithm

This paper proposed and evaluates a QOS routing policy in packets topology and irregular traffic of communications network called K-shortest Widest paths Q-Routing. The technique used for the evaluation signals of reinforcement is Q-learning. Compared to standard Q-Routing, the exploration of paths is limited to K best non loop paths in term of hops number (number of routers in a path) leading to a substantial reduction of convergence time. In this work a proposal for routing which improves the delay factor and is based on the Reinforcement Learning is Concerned . The simplest policy is the shortest-path algorithm, which always routes packets through the path with the minimum number of hops. This policy is not always good because some intermediate nodes, falling in a popular route, might have large queues. In such cases it would be better to send the packet through another route that may be longer in terms of hops but results in shorter delivery time. Hence as the traffic builds up at some popular routes , alternative routes must be chosen to keep the average packet delivery time low. This is the key motivation for adaptive packet routing strategies that learn alternate routes through exploration as the current routing policy begins to lead to degraded performance. We use Q-Learning as the Reinforcement Learning technique and introduce K-Shortest idea into the Learning Process. The proposed algorithm are applied to two different topologies. The OPNET is used to evaluate the performance of the proposed algorithm. The algorithm evaluation is done for two traffic Conditions, namely low load and high load.