Machine Learning Applications in Kubernetes for Autonomous Container Management

Abstract

This study investigates the incorporation of machine learning (ML) into Kubernetes to improve autonomous container management. Our primary focus is to explore the potential of machine learning in enhancing predictive auto-scaling, resource optimization, and self-healing capabilities in Kubernetes environments. This study thoroughly examines current research, consolidates significant discoveries, and highlights developing patterns. Our approach thoroughly examines various secondary data sources, such as academic articles, industry reports, and case studies. Our research has uncovered some fascinating insights into the impact of machine learning on predictive analytics and resource management in Kubernetes clusters. The results show a clear improvement in performance, efficiency, and reliability. In addition, techniques for autonomously detecting and resolving anomalies can help minimize downtime and operational disruptions. Nevertheless, there are still obstacles to overcome, including ensuring data quality, managing computational overhead, and addressing the demand for explainable AI. Policy implications involve strong data governance, transparent AI decision-making, and investment in scalable infrastructure. This study suggests that ML applications in Kubernetes have the potential to bring about significant changes, leading to more intelligent, robust, and efficient cloud-native operations. Organizations can maximize the advantages of autonomous container management in Kubernetes environments by acknowledging current constraints and embracing new developments such as federated learning and multi-cloud orchestration.