Event-Driven Real-Time Autoscaling Mechanisms in Kubernetes

This study highlights the pivotal role of Kubernetes autoscaling in enhancing the performance of containerized environments, addressing the limitations of traditional scaling methods, especially in adapting to the dynamic traffic patterns of modern cloud-native applications. To tackle these challenges, the research proposes a customized metric scaler designed to enhance Kubernetes’ Horizontal Pod Autoscalers. By leveraging customized metrics derived from message queue traffic, this innovation optimizes application performance and enables dynamic workload scaling. To facilitate seamless integration and agile workload management, the research presents a microservices architecture comprising essential components such as Producer, RabbitMQ, Consumer, Decision, and Kubernetes Services. Furthermore, it showcases the application of this system across various domains including e-commerce platforms, real-time analytics, IoT data processing, and microservices orchestration. With its expanded support for metrics integration and insights into future scalability and adaptability enhancements, this research represents a significant stride towards efficient Kubernetes clusters.