Novel reinforcement learning-based approaches to reduce loss probability in buffer-less OBS networks

Authors:
Abdeltouab Belbekkouche;Abdelhakim Hafid;Michel Gendreau
Affiliations:
Network Research Laboratory, University of Montreal, Montreal, Canada;Network Research Laboratory, University of Montreal, Montreal, Canada;CIRRELT, University of Montreal, Montreal, Canada
Venue:
Computer Networks: The International Journal of Computer and Telecommunications Networking
Year:
2009

Citing 8
Cited 4

Technical Note: \cal Q-Learning

Machine Learning
Optical burst switching (OBS) - a new paradigm for an optical Internet

Journal of High Speed Networks - Special issue on optical networking
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
On deflection routing in optical burst-switched networks

Journal of High Speed Networks
Reinforcement learning: a survey

Journal of Artificial Intelligence Research
Path switching in OBS networks

NETWORKING'05 Proceedings of the 4th IFIP-TC6 international conference on Networking Technologies, Services, and Protocols; Performance of Computer and Communication Networks; Mobile and Wireless Communication Systems
Contention-Based Limited Deflection Routing Protocol in Optical Burst-Switched Networks

IEEE Journal on Selected Areas in Communications
A Reinforcement Learning Framework for Path Selection and Wavelength Selection in Optical Burst Switched Networks

IEEE Journal on Selected Areas in Communications - Part Supplement

Wireless mesh and optical burst switching convergence for a novel metropolitan area network architecture

Computer Networks: The International Journal of Computer and Telecommunications Networking
On Delay Performance and Burst Assembly for Wireless Mesh and Optical Burst Switching Converged Metro Area Network

Mobile Networks and Applications
Reinforcement learning based routing in wireless mesh networks

Wireless Networks
Self-healing in transparent optical packet switching mesh networks: A reinforcement learning perspective

Computer Networks: The International Journal of Computer and Telecommunications Networking

Quantified Score

Hi-index	0.00

Visualization

Abstract

Optical Burst Switching (OBS) is a promising switching paradigm for the next generation Internet. A buffer-less OBS network can be implemented simply and cost-effectively without the need for either wavelength converters or optical buffers which are, currently, neither cost-effective nor technologically mature. However, this type of OBS networks suffers from relatively high loss probability caused by wavelength contentions at core nodes. This could prevent or, at least, delay the adoption of OBS networks as a solution for the next generation optical Internet. To enhance the performance of buffer-less OBS networks, we propose three approaches: (a) a reactive approach, called Reinforcement Learning-Based Deflection Routing Scheme (RLDRS) that aims to resolve wavelength contentions, after they occur, using deflection routing; (b) a proactive multi-path approach, called Reinforcement Learning-Based Alternative Routing (RLAR), that aims to reduce wavelength contentions; and (c) an approach, called Integrated Reinforcement Learning-based Routing and Contention Resolution (IRLRCR), that combines RLAR and RLDRS to conjointly deal with wavelength contentions proactively and reactively. Simulation results show that both RLAR and RLDRS reduce, effectively, loss probability in buffer-less OBS networks and outperform the existing multi-path and deflection routing approaches, respectively. Moreover, simulation results show that a substantial performance improvement, in terms of loss probability, is obtained using IRLRCR.