SIAM Journal on Computing
Reinforcement Learning
Networking with Cognitive Packets
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Confidence Based Dual Reinforcement Q-Routing: An adaptive online network routing algorithm
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Scalable Performance Signalling and Congestion Avoidance
Scalable Performance Signalling and Congestion Avoidance
Computer Networks: The International Journal of Computer and Telecommunications Networking
Ant Colony Optimization
Policy-Based Network Management: Solutions for the Next Generation (The Morgan Kaufmann Series in Networking)
International Journal of Communication Systems
Quality-of-service routing for supporting multimedia applications
IEEE Journal on Selected Areas in Communications
QRON: QoS-aware routing in overlay networks
IEEE Journal on Selected Areas in Communications
Reinforcement learning in multi-agent environment and ant colony for packet scheduling in routers
Proceedings of the 5th ACM international workshop on Mobility management and wireless access
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Inductive routing based on energy and delay metrics in wireless sensor networks
Proceedings of the 6th International Wireless Communications and Mobile Computing Conference
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In the context of modern high-speed internet network, routing is often complicated by the notion of guaranteed Quality of Service (QoS), which can either be related to time, packet loss or bandwidth requirements: constraints related to various types of QoS make some routing unacceptable. 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 called N best optimal path Q-routing algorithm (NOQRA) which optimizes two criteria: cumulative cost path (or hop count if each link cost=1) and end-to-end delay. A load balancing policy depending on a dynamical traffic path probability distribution function is also defined and embodied in NOQRA to characterize the distribution of the traffic over the N best paths. Numerical results obtained with OPNET simulator for different packet interarrival times statistical distributions with different levels of traffic's load show that NOQRA gives better results compared to standard optimal path routing algorithms.