Abstract

Intelligent container orchestration using machine learning provides an effective approach for optimizing dynamic workloads in cloud computing environments. Traditional container orchestration systems rely primarily on predefined rules and static resource-allocation policies, which may result in resource underutilization, performance degradation, and increased operational costs when workload patterns change rapidly. This framework integrates machine learning with container orchestration to enable predictive, adaptive, and automated workload management. Real-time metrics such as CPU utilization, memory consumption, network traffic, response time, and workload demand are continuously collected from containerized applications. Machine learning models analyze these metrics to predict future resource requirements and support intelligent decisions regarding container placement, scaling, scheduling, and resource allocation. Reinforcement learning can further enable the orchestration system to learn optimal policies through continuous interaction with the cloud environment. Automated horizontal and vertical scaling mechanisms dynamically adjust container resources according to workload variations while maintaining application performance and service-level objectives