AI-Powered Kubernetes Orchestration for Complex Cloud-Native Workloads

Abstract

The high rate at which cloud-native applications and microservices are being adopted has created the need to create sophisticated orchestration solutions capable of handling complex workloads. Kubernetes is the most popular container orchestration system that has transformed the process of deploying and scaling of cloud-native applications. Nevertheless, large scale and dynamic environments are more difficult to manage, which introduces the problem of resource optimization, fault tolerance, and operational efficiency. In this study, the researcher will suggest an AI-based Kubernetes orchestration architecture that will help to improve management of complex cloud-native workloads. With the combination of machine learning (ML) algorithms and Kubernetes, the framework provides automated resource allocation, load balancing, and prediction of failure which greatly enhance the efficiency of the operations and minimise human interventions. The solution that is proposed uses reinforcement learning to find the dynamic scaling solutions and anomaly detection, whereas predictive analytics is used to ensure the provisioning of the resources in the best way and reducing the downtime. The paper outlines the architecture of the AI-assisted Kubernetes system, the incorporation of the ML models into the control plane of the Kubernetes system, and the performance of the system compared to the standard Kubernetes systems. The findings depict that the AI-enhanced system has an enhanced resource utilization, latency, and fault tolerance. The results indicate a solution to the problem of the cloud-native environment in the form of AI, and it is a scaled and resilient solution to the modern cloud-based applications