Deflection routing in complex networks

Contention is the main source of information loss in buffer-less network architectures where deflection routing is a viable contention resolution scheme. In recent years, various reinforcement learning-based deflection routing algorithms have been proposed. However, performance of these algorithms has not been evaluated in larger networks that resemble the autonomous system-level topology of the Internet. In this paper, we compare performance of three reinforcement learning-based deflection routing algorithms by using topologies generated with Waxman and Barabási-Albert algorithms. We examine the scalability of deflection routing algorithms by increasing the network size while keeping the network load constant.