Dynamic and Forecast-Based Containers Autoscaling for Kubernetes with Reinforcement Learning
Alfredo Lipari, Gabriele Proietti Mattia, Roberto Beraldi · 2025
Efficient resource management in Kubernetes is crucial for optimizing performance and cost in cloud computing environments. Traditional autoscaling methods react to workload changes but often fail to predict them, leading to underutilization of resources or performance degradation. This paper introduces a model-free Reinforcement Learning (RL) based autoscaler that leverages a deep Q-Network (DQN) alongside a Long Short-Term Memory (LSTM) network for workload forecasting. By predicting future request rates, our autoscaler proactively adjusts the number of container replicas, ensuring compliance to Service Level Objectives (SLOs) while minimizing resource usage. Experimental results demonstrate that our approach outperforms standard Kubernetes auto-scaling strategies, achieving a resource consumption reduction of up to 10% and improving performance levels up to 30% compared to the default auto-scaling algorithm.