Event Driven Autoscaling Strategies for Kubernetes Applications

This study emphasizes the critical role that Kubernetes autoscaling plays in improving the performance of containerized environments. It overcomes the drawbacks of conventional scaling techniques, particularly when it comes to adjusting to the real-time message queue traffic patterns of contemporary cloud-native apps. The research suggests a customized metric scaler that improves on Kubernetes’ Horizontal Pod Autoscaler in order to address these issues. Through the use of customized metrics from message queue traffic, this innovation optimizes application performance and enables dynamic workload scaling. In order to facilitate smooth integration and agile workload management, the research offers a microservices architecture that incorporates essential elements such as Producer, RabbitMQ, Consumer, Decision, and Kubernetes Services. Additionally, it displays the system’s applications in e-commerce platforms, real-time analytics, IoT data processing, and microservices orchestration. With its integration expansion of supported metrics, and outline of upcoming scalability and adaptability advancements, this research represents a major step towards efficient Kubernetes clusters..