Name
Abstract | ||
|---|---|---|
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 which optimizes two criteria: cumulative
cost path and end-to-end delay. Numerical results obtained with OPNET simulator
for different packet interarrival times statistical distributions with
different levels of traffic's load show that the proposed approach gives better
results compared to standard optimal path routing algorithms. |
Year | Venue | Keywords |
|---|---|---|
2008 | Computing Research Repository | qos routing,min. end-to-end delay,q-routing,routage avec qos,mots-cles. routage multi chemins,rein- forcement learning.,apprentissage par renforcement. keywords. multi path routing,minimisation du délai de bout en bout |
DocType | Volume | Citations |
Journal | abs/0803.0 | 0 |
PageRank | References | Authors |
0.34 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Said Hoceini | 1 | 106 | 13.84 |
| Abdelhamid Mellouk | 2 | 679 | 75.86 |
| Hayet Hafi | 3 | 0 | 0.34 |