A reinforcement learning-based lightpath establishment for service differentiation in all-optical WDM networks

Authors:
Izumi Koyanagi;Takuji Tachibana;Kenji Sugimoto
Affiliations:
Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan;Graduate School of Information Science, Nara Institute of Science and Technology, Nara, Japan
Venue:
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Year:
2009

Citing 9
Cited 1

Introduction to Reinforcement Learning

Introduction to Reinforcement Learning
Efficient QoS provisioning for adaptive multimedia in mobile communication networks by reinforcement learning

Mobile Networks and Applications - Special issue: Recent advances in wireless networking
G-lambda: coordination of a grid scheduler and lambda path service over GMPLS

Future Generation Computer Systems - IGrid 2005: The global lambda integrated facility
Reinforcing probabilistic selective Quality of Service routes in dynamic irregular networks

Computer Communications
Detecting Primary Signals for Efficient Utilization of Spectrum Using Q-Learning

ITNG '08 Proceedings of the Fifth International Conference on Information Technology: New Generations
A multi-area MPLS/GMPLS interoperability trial over ROADM/OXC network

IEEE Communications Magazine
Cognitive network management with reinforcement learning for wireless mesh networks

IPOM'07 Proceedings of the 7th IEEE international conference on IP operations and management
Adaptive call admission control under quality of service constraints: a reinforcement learning solution

IEEE Journal on Selected Areas in Communications
Service-specific resource allocation in WDM networks with quality constraints

IEEE Journal on Selected Areas in Communications

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

In this paper, we propose a lightpath establishment method based on reinforcement learning for providing the service differentiation in all-optical WDM networks. In our proposed method, the optimal policy for the lightpath establishment is derived with Q-learning. With the derived policy, each node decides whether a lightpath establishment request of each class should be accepted or not. This method can be available even if the number of wavelengths is large and there is no assumption about the lightpath establishment. We also discuss how the proposed method is utilized with Generalized Multi-Protocol Label Switching (GMPLS). In numerical examples, we investigate the impacts of learning parameters on the performance of the proposed method. Then, we show that our proposed method can provide the service differentiation for the lightpath blocking probability, while utilizing wavelengths effectively.