Author(s) :   Anjaiah Keesari

Abstract : The rapid evolution of cloud-native technologies has fundamentally transformed the design, deployment, and scalability of enterprise systems. Among these advancements, event-driven architectures have emerged as a cornerstone for building responsive, resilient, and highly scalable microservices. This paper presents a comprehensive study and empirical performance analysis of high-throughput cloud-native messaging architectures, focusing on the integration of Azure Kubernetes Service (AKS) and Azure Event Hub within a publish-subscribe (pub/sub) model for asynchronous communication. The proposed architecture leverages Kubernetes for dynamic orchestration, containerized publisher and subscriber microservices for distributed message processing, and Azure Event Hub as a managed, horizontally scalable messaging backbone. PostgreSQL is employed as the persistence layer to ensure durable state management and transactional consistency. A key contribution of this work is the systematic evaluation of the system’s scalability, throughput, and latency under varying load conditions, using controlled experiments and real-world workload simulations. Our findings highlight the operational efficiency achieved through Kubernetes auto-scaling, partition-based message distribution, and asynchronous consumption patterns. The results demonstrate that cloud-native designs can effectively handle large-scale data ingestion and real-time event streaming workloads with minimal latency, while maintaining high reliability and fault tolerance. Furthermore, the study identifies best practices for integrating observability, security, and automation into the messaging architecture, ensuring compliance and operational transparency. By bridging the gap between theoretical models and practical implementation, this paper provides actionable insights and reference architecture for architects and engineers seeking to design or optimize high-performance event-driven microservices in modern cloud environments. The implications of this research extend to a wide range of industries requiring robust, scalable, and efficient real-time data processing solutions.

DOI: 10.61161/ijarcsms.v13i11.1

Pages : 1-20

*Authors are invited to submit papers through E-mail at editor.ijarcsms@gmail.com