Latency and Bandwidth-Aware Orchestrator for QoS-Sensitive Applications Using a Reinforcement Learning-Based Scheduler with Kubernetes

In the realm of Fifth Generation (5 G) and the upcoming Sixth Generation (6 G) networks, the efficient management of network resources becomes increasingly critical, particularly for applications that have strict Quality of Service (QoS) requirements. This paper addresses the complexities associated with Virtual Network Embedding (VNE), a vital process for establishing multiple virtual networks on shared physical infrastructure within the context of network slicing. We introduce the SetpodNet scheduler, a novel orchestration solution that leverages reinforcement learning to enhance the optimization of latency and bandwidth allocation specifically in Kubernetes environments. The SetpodNet scheduler is designed to dynamically adapt to fluctuating slice arrivals and varying resource demands, ensuring that network performance remains consistent and reliable. Through comprehensive experimental evaluations, we demonstrate improvements in slice acceptance ratios and optimizing QoS.