Paper Info
Title
Adaptive Quality Of Service-Based Routing Approaches: Development Of Neuro-Dynamic State-Dependent Reinforcement Learning Algorithms
Abstract
In this paper, we propose two adaptive routing algorithms based on reinforcement learning. In the first algorithm, we have used a neural network to approximate the reinforcement signal, allowing the learner to take into account various parameters such as local queue size, for distance estimation. Moreover, each router uses an online learning module to optimize the path in terms of average packet delivery time, by taking into account the waiting queue states of neighbouring routers. In the second algorithm, the exploration of paths is limited to N-best non-loop paths in terms of hops number (number of routers in a path), leading to a substantial reduction of convergence time. The performances of the proposed algorithms are evaluated experimentally with OPNET simulator for different levels of traffic's load and compared with standard shortest-path and Q-routing algorithms. Our approach proves superior to classical algorithms and is able to route efficiently even when the network load varies in an irregular manner. We also tested our approach on a large network topology to proof its scalability and adaptability. Copyright (c) 2006 John Wiley & Sons, Ltd.
Year
DOI
Venue
2007
10.1002/dac.858
INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS
Keywords
Field
DocType
shortest-path routing, flow-based routing, N-best paths, neural networks, adaptive routing, neuro-dynamic state-dependent, reinforcement learning, traffic engineering
Convergence (routing),Equal-cost multi-path routing,Computer science,Static routing,Path vector protocol,Routing domain,Computer network,Algorithm,Source routing,Routing protocol,Reinforcement learning,Distributed computing
Journal
Volume
Issue
ISSN
20
10
1074-5351
Citations 
PageRank 
References 
12
0.63
7
Authors
3
Name
Order
Citations
PageRank
Abdelhamid Mellouk167975.86
Said Hoceini210613.84
Yacine Amirat363968.98