Real-Time JVM Optimization in Apache Kafka Streaming: A Continuous Deployment Framework using Machine Learning

It is now crucial for enterprises to optimize Apache Kafka for high-performance data processing since real-time data streaming is becoming more and more important for key processes. It is difficult to adjust Java Virtual Machine (JVM) parameters for Kafka, and the ideal configurations varies greatly depending on the workload and environment. In this research, in order to efficiently deploy Machine Learning models that constantly modify JVM parameters, an automated Continuous Integration/Continuous Deployment (CI/CD) pipeline for Apache Kafka data streaming applications is proposed. By combining model training, testing, and validation into a repeatable pipe-line, proposed CI/CD methodology enables quick cycles of experimentation and deployment. With the help of Docker and Kubernetes for containerized, scalable systems, the pipeline minimizes manual intervention and ensures realtime adaptability by automating model retraining in response to changes in Kafka workload characteristics. The effectiveness is demonstrated by the evaluation findings.