An Adaptive Reinforcement Learning-based Approach to Reduce Blocking Probability in Bufferless OBS Networks

Abstract

Optical burst switching (OBS) is an optical switching paradigm which offers a good tradeoff between the traditional optical circuit switching (OCS) and optical packet switching (OPS) since it has the relatively easy implementation of the first and the efficient bandwidth utilization of the second. Hence, OBS is a promising technology for the next generation optical Internet. A buffer-less OBS network can be implemented using ordinary optical communication equipment without the need for either wavelength converters or optical memories. However, OBS networks suffer from a relatively high blocking probability, a primary metric of interest, because of contention. In this paper we propose a new contention resolution scheme for buffer-less OBS networks using deflection routing and reinforcement learning agents to dynamically assign an appropriate offset time (OT) to each burst in order to reduce losses caused, for example, by insufficient offset time (IOT) in case only deflection is used. Simulation results demonstrate that our approach reduces effectively blocking probability, whereas it maintains a reasonable end-to-end delay for each burst. Hence, it establishes an appropriate tradeoff between loss rate and delay.