Towards Cognitive Routing based on Deep Reinforcement Learning
This addresses the need for more adaptive routing methods in network infrastructure due to growing traffic and service changes, but it appears incremental as it builds on existing DRL techniques.
The paper tackles the problem of intelligent routing in networks by proposing a cognitive routing definition and implementing a Deep Reinforcement Learning (DRL)-based approach, showing that their DDPG-based algorithm outperforms OSPF and random weight algorithms in simulations on an example network topology.
Routing is one of the key functions for stable operation of network infrastructure. Nowadays, the rapid growth of network traffic volume and changing of service requirements call for more intelligent routing methods than before. Towards this end, we propose a definition of cognitive routing and an implementation approach based on Deep Reinforcement Learning (DRL). To facilitate the research of DRL-based cognitive routing, we introduce a simulator named RL4Net for DRL-based routing algorithm development and simulation. Then, we design and implement a DDPG-based routing algorithm. The simulation results on an example network topology show that the DDPG-based routing algorithm achieves better performance than OSPF and random weight algorithms. It demonstrate the preliminary feasibility and potential advantage of cognitive routing for future network.