Open Access

Optimizing Zero-Downtime Microservice Deployments: Integrating DevOps Principles in .NET Core Environments

4 Faculty of Engineering, Technical University of Munich, Germany

Abstract

In contemporary software engineering, the demand for continuous availability and rapid deployment has propelled the adoption of zero-downtime strategies, particularly within microservice architectures. This research examines the intersection of DevOps methodologies and .NET Core microservices to achieve seamless deployment of enterprise authentication platforms, with a specific focus on AuthHub systems. Drawing on both theoretical frameworks and empirical studies, the article critically explores the challenges of migrating legacy monolithic systems to modular microservice architectures without service interruption. The study investigates the role of automated pipelines, database schema evolution, and container orchestration in minimizing downtime, emphasizing the practical implementation strategies that ensure high reliability and maintainability. The research identifies key enablers of zero-downtime deployment, including continuous integration, continuous delivery, feature toggling, and blue-green deployment strategies. Furthermore, it discusses the implications of NoSQL database integration, graph-oriented storage, and transactional consistency in distributed environments. Counter-arguments surrounding the complexity of orchestration, risk of deployment failure, and potential performance degradation are examined, alongside rebuttals grounded in contemporary DevOps and software architecture literature. The findings suggest that structured adoption of DevOps principles within .NET Core microservices can significantly reduce operational downtime, optimize resource utilization, and enhance system resilience. The research contributes to the scholarly discourse by synthesizing cross-disciplinary perspectives, offering a roadmap for technology organizations aiming to reconcile rapid delivery with system stability, and highlighting areas for future investigation, including automated anomaly detection, AI-assisted deployment orchestration, and hybrid data consistency models.

Keywords

References

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