Adaptive Routing and Contention Resolution approaches for OBS networks with QoS differentiation

Optical Burst Switching (OBS) is a promising switching paradigm for the next generation Internet. A bufferless 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 issue 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 two approaches: a) a proactive multi-path approach, called Reinforcement Learning-based Alternative Routing (RLAR), that tries to prevent wavelength contentions; and b) an approach, called Integrated Reinforcement Learning-based Routing and Contention Resolution (IRLRCR) that deals with wavelength contentions proactively and reactively conjointly. We also show relative QoS differentiation capability of the proposed schemes. Simulation results show the ability of the proposed approaches to reduce loss probability and to provide Quality of Service (QoS) differentiation.