Reinforcement Learning Based Path Selection and Wavelength Selection in Optical Burst Switched Networks

Abstract

Optical burst switching (OBS) is a promising technology that exploits the benefits of optical communication and supports statistical multiplexing of data traffic at a fine granularity making it a suitable technology for the next generation Internet. Development of efficient algorithms for path selection and wavelength selection is crucial in minimizing the burst loss probability (BLP) in OBS networks. In this paper, we present novel Reinforcement Learning algorithms for path selection and wavelength selection in the context of OBS networks. We develop an online path selection algorithm based on Q-learning to minimize the BLP by choosing an optimal path among a set of predetermined routes between every pair of ingress and egress nodes. We also propose a Q-learning algorithm for wavelength selection that selects an optimal wavelength among the available wavelengths in a pre-routed path with an objective of minimizing the BLP. We assume no wavelength conversion and buffering to be available at the core nodes of the OBS network. We simulate the proposed algorithms under dynamic load to demonstrate that they reduce the BLP compared to the best known adaptive techniques for path selection and wavelength selection available in the literature.