Imitation Learning Based Heavy-Hitter Scheduling Scheme in Software-Defined Industrial Networks

Abstract

To realize flexible networking and on-demand topology reconstructing, Software-Defined Industrial Networks(SDIN) are increasingly embracing the flat structure. Similar to SDN, SDIN suffers from low traffic scheduling efficiency caused by large and imbalanced flows, known as the heavy hitters problem. Due to such heavy hitters, industrial networks may fail to satisfy application's QoS requirements, which results in more severe damages. To improve flow scheduling efficiency under heavy hitters, this article introduces a novel Imitation Learning-based Flow Scheduling (ILFS) method. ILFS utilizes P4-based In-band Network Telemetry(INT) technology to collect fine-grained, real-time traffic data from SDIN's data plane. In the control plane, it integrates the Generative Adversarial Imitation Learning (GAIL) model with a Soft Actor Critic (SAC) to preserve the experiences of flow, thereby better scheduling large flows. Our experiments thoroughly compare ILFS's performance with several state-of-the-art traffic scheduling strategies. The results indicate that ILFS successfully controls the link bandwidth utilization between 10% and 80% and significantly improves the average network throughput and link utilization rate.