DRLNPS: A deep reinforcement learning network path switching solution

Abstract

SummaryThis paper proposes a solution to the problem of switching between different network paths. We choose to switch between multiprotocol label switching (MPLS) and software‐defined wide area networking (SD‐WAN) connections specifically as they are the mainstream currently. The solution should maintain a service license agreement (SLA) while choosing SD‐WAN as long as possible to save cost. Therefore, a deep reinforcement learning solution is proposed that predicts when to switch based on bandwidth availability and quality of service (QoS) parameters like jitter and delay. Results show that double deep Q learning in combination with these parameters are suitable to make a sophisticated decision on link switching between MPLS and SD‐WAN.