THE ROLE OF MACHINE LEARNING IN CONTAINER ORCHESTRATION: SMARTER AUTOSCALING AND MONITORING

Abstract

Container orchestration systems like Kubernetes have become the backbone of modern cloud-native applications, enabling automated deployment, scaling, and management of containerized applications. However, traditional rule-based approaches to autoscaling and monitoring face challenges in dynamic workload environments, often leading to resource inefficiencies or performance degradation. This paper explores how machine learning techniques can enhance container orchestration with smarter, more adaptive mechanisms. The research investigates reinforcement learning models for predictive autoscaling, anomaly detection for proactive monitoring, and time series forecasting for resource optimization. Experimental results demonstrate that ML-augmented orchestration systems can achieve up to 27% better resource utilization while reducing SLA violations by 18% compared to threshold-based approaches. Additionally, the implementation of ML-based anomaly detection identified 92% of performance issues before they affected user experience. The findings suggest that integrating machine learning with container orchestration provides significant advantages in managing the complexity and dynamism of modern microservices environments, though challenges remain in training data requirements and real-time inference capabilities.