Intelligent Routing Algorithm over SDN: Reusable Reinforcement Learning Approach
This work addresses network efficiency for internet users and operators, but it is incremental as it builds on existing reinforcement learning methods for routing.
The paper tackles the problem of adaptive traffic routing in Software-Defined Networks (SDN) by developing a reusable reinforcement learning algorithm, RLSR-Routing, which learns network QoS status to speed up convergence for multiple traffic demands and achieves better load balancing than traditional approaches.
Traffic routing is vital for the proper functioning of the Internet. As users and network traffic increase, researchers try to develop adaptive and intelligent routing algorithms that can fulfill various QoS requirements. Reinforcement Learning (RL) based routing algorithms have shown better performance than traditional approaches. We developed a QoS-aware, reusable RL routing algorithm, RLSR-Routing over SDN. During the learning process, our algorithm ensures loop-free path exploration. While finding the path for one traffic demand (a source destination pair with certain amount of traffic), RLSR-Routing learns the overall network QoS status, which can be used to speed up algorithm convergence when finding the path for other traffic demands. By adapting Segment Routing, our algorithm can achieve flow-based, source packet routing, and reduce communications required between SDN controller and network plane. Our algorithm shows better performance in terms of load balancing than the traditional approaches. It also has faster convergence than the non-reusable RL approach when finding paths for multiple traffic demands.