Joint path and wavelength selection using Q-learning in optical burst switching networks

Authors:
T. Venkatesh;Y. V. Kiran;C. Siva Ram Murthy
Affiliations:
Dept. of Comp. Sci. and Engg., IIT Madras, Chennai, India;Create-Net International Research Center, Trento, Italy;Dept. of Comp. Sci. and Engg., IIT Madras, Chennai, India
Venue:
ICC'09 Proceedings of the 2009 IEEE international conference on Communications
Year:
2009

Citing 9
Cited 0

Technical Note: \cal Q-Learning

Machine Learning
Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
On the convergence of stochastic iterative dynamic programming algorithms

Neural Computation
Reinforcement learning: a survey

Journal of Artificial Intelligence Research
Optical Burst Switched Networks

Optical Burst Switched Networks
Control architecture in optical burst-switched WDM networks

IEEE Journal on Selected Areas in Communications
Wavelength Selection in OBS Networks Using Traffic Engineering and Priority-Based Concepts

IEEE Journal on Selected Areas in Communications
A First Step Toward Autonomic Optical Burst Switched Networks

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

IEEE Journal on Selected Areas in Communications - Part Supplement

Quantified Score

Hi-index	0.00

Visualization

Abstract

Contention losses which usually do not indicate congestion is a major issue that hinders the deployment of optical burst switching (OBS) networks. Development of efficient path and wavelength selection algorithms is crucial to minimize the burst loss probability (BLP) in OBS networks. In this paper, we handle path selection and wavelength selection in a joint fashion. We formulate the problem of selecting a pair of path and wavelength jointly as a multi-armed bandit problem (MABP) and discuss the difficulties in solving MABP directly. We then rewrite the Q-learning formalism to solve the MABP without explicit model in an online fashion and propose an algorithm to solve the problem near-optimally. The proposed algorithm selects a pair of path and wavelength at each ingress node to minimize the BLP on the long run. Simulation results demonstrate the effectiveness of our algorithm in minimizing the BLP with better link utilization compared to the other proposals in the literature.