Kubernetes

This article presents the main findings from an in-depth study of MLFlow design concepts in containerized and cloud-native systems. The research focuses on how MLFlow, as a core MLOps framework, can be efficiently deployed and managed in cloud- native environments to ensure scalable, reproducible, and secure machine learning workflows. The study analyzes the architectural principles and integration patterns of MLFlow components: Tracking Server, Projects, Models, and Registry within distributed containerized infrastructures.

An approach to counteracting cyberattacks based on state machines within a microservice architecture is suggested. It focuses on intelligent analysis of actual and possible intrusions. The approach is devised for applications with a microservice architecture deployed on the Kubernetes platform. For purposes of the study, a special dataset has been developed. We have reproduced selected common vulnerabilities and exposures reported in 2024 and collected network traffic of intrusion cyberattacks based on them.

The article delves into the intricate challenge of scaling microservices within a Kubernetes cluster, thoroughly examining existing methodologies for scaling microservice architectures, and presenting a novel approach that involves migrating specific components. Unlike the conventional horizontal and vertical scaling strategies, which require additional resources, this proposed method focuses on migrating non-critical components to another Kubernetes cluster.