RL-Based Speculative Installation of Unseen Flows in SDNs for Low-Latency Applications

Reactive software-defined networking (SDN) is an approach where flows are installed dynamically. However, when a new flow arrives, a miss occurs and causes a Packet-in message to be sent from the switch to the SDN controller. Although it allows flexible flow management, this design choice causes additional delays that may be intolerable for low-latency applications that require millisecond-level response times. We present a novel speculative SDN framework that incorporates reinforcement learning (RL) to predict the arrival of flows that may not have been seen before. We show that the RL agents can learn and speculatively install the unseen flow rules to avoid the additional control latency from the reactive installation of the flow rules.