Reinforcement learning-driven Kubernetes autoscaling for high-throughput 5G Network Functions

This article presents a novel approach to Kubernetes autoscaling for 5G network functions using reinforcement learning techniques. Traditional threshold-based autoscaling mechanisms in Kubernetes environments have shown significant limitations when handling the complex dynamics of 5G workloads, particularly in scenarios requiring network slicing and guaranteed resource allocation. The solution introduces a deep reinforcement learning-based system to address these challenges, incorporating domain-specific optimizations for 5G environments. The proposed architecture leverages deep Q-learning algorithms to create an intelligent scaling system that learns and adapts to emerging traffic patterns while maintaining strict performance requirements. Experimental results demonstrate substantial improvements in resource utilization, service reliability, and scaling efficiency compared to conventional approaches while effectively managing multiple concurrent network slices with varying quality of service requirements.