DRSIR: A Deep Reinforcement Learning Approach for Routing in
Software-Defined Networking
Abstract
Traditional routing protocols employ limited information to make routing
decisions which leads to slow adaptation to traffic variability and
restricted support to the quality of service requirements of the
applications. To address these shortcomings, in previous work, we
proposed RSIR, a routing solution based on Reinforcement Learning (RL)
in SoftwareDefined Networking (SDN). However, RL-based solutions usually
suffer an increase in the learning process when dealing with large
action and state spaces. This paper introduces a different routing
approach called Deep Reinforcement Learning and SoftwareDefined
Networking Intelligent Routing (DRSIR). DRSIR defines a routing algorithm
based on Deep RL (DRL) in SDN that overcomes the limitations of RL-based
solutions. DRSIR considers path-state metrics to produce proactive,
efficient, and intelligent routing that adapts to dynamic traffic changes.
DRSIR was evaluated by emulation using real and synthetic traffic
matrices. The results show that this solution outperforms the routing
algorithms based on the Dijkstra’s algorithm and RSIR, in relation to
stretching (stretch), packet loss, and delay. Moreover, the results
obtained demonstrate that DRSIR provides a practical and viable solution
for routing in SDN.