Optimizing Kubernetes Autoscaling with Artificial Intelligence

This study explores how to improve Kubernetes auto-scaling using artificial intelligence based forecasting. The authors emphasize the limitations of traditional, reactive auto-scaling methods that lag behind rapid changes in demand and propose a proactive approach that predicts future resource requirements. The paper presents a framework for integrating artificial intelligence based predictions into the Kubernetes ecosystem to improve operational efficiency and resource utilization. To address the main challenges, the authors focus on improving workload forecasting and mitigating the impact of random fluctuations in Kubernetes performance. To address this issue, they use time-series forecasting models combined with data preprocessing techniques to predict future CPU utilization and thus inform scaling decisions before peaks or troughs in demand occur. The results show that artificial intelligence based forecasting can significantly improve scaling accuracy, reduce latency, and optimize resource utilization in Kubernetes environments. Time-series models are developed and evaluated using real CPU utilization data from a Kubernetes cluster, including RNN, LSTM, and CNN-GRU. The study also explores new architectures such as Fourier Analysis Network and Kolmogorov–Arnold Network and their integration with the transformer model. In general, the proposed approach aims to improve resource efficiency and application reliability in Kubernetes through proactive automatic scaling.