Distributed Quality-of-Service routing of best constrained shortest paths.

High speed modern communication networks are required to integrate and support multimedia application which requires differentiated Quality-of-Service (QoS) guarantees. Routing mechanism is a key to success of future communication networking. However, it is often complicated by the notion of guaranteed QOS, which can either be related to time, cost, packet loss or bandwidth requirements. Communication network requires that as the load levels, traffic patterns and topology of the network change, the routing policy also adapts. In this paper, we present a QoS based routing to construct dynamic state-dependent routing policies. The proposed algorithm used a reinforcement learning paradigm to optimize two QoS criteria: cumulative cost path based on hop count and end-to-end delay. Multiple paths are searched in parallel to find the N best qualified ones. In order to improve the overall network performance, a load balancing policy is defined and depends on a dynamical traffic path probability distribution function. The performance of our algorithm for different levels of traffic’s load is compared experimentally with standard optimal path routing algorithms for the same problem.