Congestion avoidance and control
SIGCOMM '88 Symposium proceedings on Communications architectures and protocols
Technical Note: \cal Q-Learning
Machine Learning
Quality of service based routing: a performance perspective
Proceedings of the ACM SIGCOMM '98 conference on Applications, technologies, architectures, and protocols for computer communication
SIAM Journal on Computing
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neural networks in B-ISDN flow control: ATM traffic prediction or network modeling?
IEEE Communications Magazine
A swarm intelligent multi-path routing for multimedia traffic over mobile ad hoc networks
Proceedings of the 1st ACM international workshop on Quality of service & security in wireless and mobile networks
QoS dynamic routing for wireless sensor networks
Proceedings of the 2nd ACM international workshop on Quality of service & security for wireless and mobile networks
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
| Hi-index | 0.00 |
Actually, various kinds of sources (such as voice, video, or data) with diverse traffic characteristics and Quality of Service Requirements (QoS), which are multiplexed at very high rates, leads to significant traffic problems such as packet losses, transmission delays, delay variations, etc, caused mainly by congestion in the networks. The prediction of these problems in real time is quite difficult, making the effectiveness of “traditional” methodologies based on analytical models questionable. This article proposed and evaluates a QoS routing policy in packets topology and irregular traffic of communications network called K-shortest 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. Moreover, each router uses an on line learning module to optimize the path in terms of average packet delivery time. The performance of the proposed algorithm is evaluated experimentally with OPNET simulator for different levels of load and compared to Q-Routing algorithm.